Sesión 1

Curso: R para usuarios de Stata

Percy Soto-Becerra, M.D., M.Sc(c)

InkaStats Academy | Medical Branch

2023

https://github.com/psotob91

Introducción a R y RStudio

Introducción a R y RStudio

Stata versus R

  • Paquete de software estadístico comercial.

  • Creado en 1985 por StataCorp.

  • Pensando para hacer ‘casi todo’ a través de comandos empaquetados en archivos ado.

  • Sin embargo, uso óptimo requiere algunas habilidades de programación.

  • Lenguaje de programación para análisis estadístico.

  • Es un software libre de R Development Core Team.

  • Pensando para hacer ‘casi todo’ a través de funciones empaquetadas en bibliotecas.

  • Uso óptimo requiere habilidades de programación un poco más tempranamente que en Stata.

R y RStudio


  • Creado en 1993 por Robert Gentleman y Ross Ihaka como una reimplementación del lenguaje S de Laboratorios Bell.

  • R es un lenguaje de programación estadística de código abierto.

  • R también es un entorno para computación estadística y gráfica.

  • Está potenciado con paquetes o bibliotecas.

  • Desarrollado por Posit, PBC, en 2010 (antes RStudio Inc.) una corporación de beneficio pública de J. J. Allair.

  • RStudio es una interfaz para R denominado entorno de desarrollo integrado (IDE por integrated development environment).

  • RStudio no es requerido para programar con R, pero es uno de los IDE más comúnmente utilizados.

El problema de R: su aspecto

R

Stata

RStudio: el chasis del carro

RStudio

Stata

En resumen, R y RStudio


En resumen, R y RStudio

Jerga básica: R vs. Stata (1/3)

Objeto: Cualquier abstracción en R. Los datos son objetos en R. Ejemplos: vectores, matrices, data.frames, listas, etc.

Función: Código capaz de realizar una acción específica con los datos. También es un objeto: un objeto que actúa sobre otros objetos.

Objeto: Stata también soporta programación orientada a objetos a través de sus lenguajes de programación. Ej: matrices, escalares, macros, etc.

Comando: Código capaz de realizar una acción específica con los datos. En Stata, es el equilvante de una función en R.

Jerga básica: R vs. Stata (2/3)

Ado: Archivos que contienne el código del programa de uno o más comandos. Son los análogos a los paquetes de R.

Ado oficial: Conjunto de archivos ado instalados oficialmente en Stata.

Paquetes: Library en inglés. Conjunto de funciones que realizan acciones a menudo relacionadas en R.

Paquetes de R base: Conjunto de paquetes instalados por defecto en R.

Jerga básica: R vs. Stata (3/3)

Ado como contribuciones: Conjunto de archivos ado no instalados oficialmente en Stata pero que puedes descargar/instalar en este.

Algunos ado han pasado por revision por pares y se encuentran publicados en el repositorio oficial de Stata y/o descritos en Stata Journal.

Paquetes de R tidy: Conjunto de paquetes que siguen el estilo tidy en R.

Otros paquetes en el CRAN de R: Paquetes que no son R base ni que necesariamente siguen filosofía tidyverse que se encuentran disponible en el repositorio oficial de R luego de haber pasado revisión por pares.

Un poco más sobre los paquetes de R

 

  • Los paquetes son las unidades fundamentales del código reproducible en R.

  • Al 13 de junio de 2022, hay 18 560 paquetes disponibles.

  • ¡Trabajaremos con un pequeño (pero importante) conjunto de estos!

  • Cada paquete contiene una o más funciones que ejecutan tareas.

  • ¡Hay paquetes de todo tipo y para todos los gustos!

R y sus paquetes


En resumen: R, RStudio y sus paquetes

tidyverse

  

tidyverse.org

  • Tidyverse es una colección grande de paquetes de R diseñados para hacer ciencia de datos: Es un metapaquete.

    • Todos los paquetes comparten una filosofía subyacente y una gramática común.

  • R, como todo lenguaje, tiene varios dialectos.

    • Dos dialectos predominan: R base y R tidy.

    • R tidy se basa en la filosofía de tidyverse.

  • Usaremos este enfoque predominantemente y R base cuando no tenga remplazo o sea más sencillo usarlo.

Aspectos elementales de R y Rstudio

Aspectos elementales de R y Rstudio

Primero instale R, R Studio y R tools

  • Si aún no ha instalado R y RStudio, revise el tutorial de instalación de R que hemos preparado para el curso:

  • Pause el video, instale y vuelva cuando todo esté listo.

Nuestro turno





10:00

Ventanas de RStudio


Scripts: El “do file” de R

Do files de Stata

R Scripts de R

Código en scripts de R


  • El código se escribe en el Script, pero no se imprime.

  • Los resultados se imprimen en la consola.

  • Aunque es útil usar la consola, es preferible usar herramientas de programación literaria:

    • RMarkdown o Quarto.

Comentarios en R Script

Stata

R

Nuestro turno


  • Haga algunos comentarios en un script de R.

  • Compare las diferencias con Stata.




10:00

Programación Literaria

Markdown y Programación Literaria

Lenguaje de marcado que permite crear una serie de tipo de documentos: html, word, pdf, etc.

  • En Ciencia de Datos se usa Markdown para crear documentos que sean:

    • Menos propensos a error de digitación.
    • Transparentes.
    • Reproducibles.

  • R, a través de RStudio y Stata permiten el uso de Markdown.

    • Sin embargo, las opciones de Stata aún son muy limitadas en comparación a RStudio.

Markdown en Stata y R

  • Hay una versión oficial poco desarrollada aún: Más info

  • Hay otros ado, contribuciones de usuarios:

  • En RStudio, RMarkdown domina(ba).

  • Recientemente, Posit anunció que dejará de mantener RMarkdown aunque todavía podrá usarse.

    • A cambio, lanzó Quarto, el RMarkdown potenciado y con las baterías precargadas.
    • Quarto recoje lo mejor de casi una década RMarkdown con otros complementos interesantes.

Markdown en Stata

R Markdown y Quarto

 

rmarkdown.rstudio.com

 

  • rmarkdown y quarto permiten a los usuarios de R escribir su código y prosa en documentos computacionales reproducibles.

  • Por lo general, nos referimos a documentos R Markdown con la extensión .Rmd y a los documentos Quarto con la extensión .qmd.

 

Archivo R Markdown (.Rmd) a PDF

R Markdown evolucionó a Quarto

  • Recientemente, RStudio presentó Quarto: https://quarto.org/.
  • Quarto es la versión mejorada de RMarkdwon.
  • Si usaste RMarkdown, puedes ejecutar tus códigos en Quarto sin ningún problema.
  • Quarto permite ejecutar código Python, Julia y Observable.

R Markdown y Quarto son muy flexibles

 

  • Herramientas de programación literaria:

    • Legibles para la computadora y para el ser humano.

  • Útiles para alcanzar reproducibilidad.

  • También muy útiles para diversas actividades académicas:

    • Presentaciones académicas

    • Artículos científicos

    • Diseño de blogs, web u otra herramienta de difusión de conocimiento.

Quarto permite crear artículos

Quarto permite crear presentaciones

De hecho, los slides de este curso están hechas en Quarto

Quarto permite crear Website

¡Sí!, la web de este curso también está hecha en Quarto

Quarto permite crear Libros

Próximamente, un libro de R para usuarios de Stata

¿Cómo usaremos Quarto en el curso?


  • Cada ejercicio / reporte / proyecto / tarea / etc. será hecho en un documento R Markdown aumentado: Quarto.

  • Siempre te proporcionaremos una plantilla de documento Quarto para iniciar.

  • Las plantillas irán modificándose conforme avancemos el curso.

  • Las plantillas son simplemente eso, plantillas, para usarlas en su día a día no necesita pensar mucho en ellas ni entenderlas a detalle.

  • Yo entiendo menos del 20% de estas y aún así me sirven mucho para mi trabajo diario. ¡Sugiero comenzar haciendo lo mismo!

Partes de un documento Quarto / R Markdown

Hagamos una pausa

Tomemos un descanso de 5 minutos…

Estire las piernas …

Deje de ver las pantallas …

… cualquier , las del celular también.

05:00

Primeros pasos en R

Primeros pasos en R

¿Rutas relativas y absolutas?

  • Esto es una ruta:

  • Las rutas permiten moverse entre carpetas:

Ruta absoluta
  • Es una ruta completa que comienza con la letra de la unidad inicial dentro de la computadora de la persona.
Ruta relativa
  • Ubicación que es relativa a un directorio actual.
  • Se fija un directorio raíz sobre el que se origian todo.

Rutas en R y Stata

pwd

getwd()

pwd “D…”

setwd(“D…”)

Nuestro turno


  • Identifique la ruta actual.

  • Cambie la ruta por la carpeta de interés.

  • Vuelva a consultar la ruta actual

  • Vuelva a cambiar la ruta por una subcarpeta.




10:00

Proyectos en R y Stata

  • Stata cuenta con gestor de proyecto que permite:

    • Organizar cualquier tipo de archivo: do file, ado file, datos, archivos planos, etc.
    • Manejar miles de archivos y carpetas por proyecto.
    • Manejar múltiples proyectos
    • Crear grupos en los proyectos para categorizar los archivos.
    • Entre otros…

  • El punto más importante es que permite usar fácilmente las rutas de carpetas/archivos relativas dado que establece un directorio raíz.

  • Tiene la misma función que en Stata.

  • También permite almacenar todos los archivos (de diversos tipos) en diversas subcarpetas.

    • Manejar miles de archivos y carpetas por proyecto.
    • Manejar múltiples proyectos
    • Crear grupos en los proyectos para categorizar los archivos.
    • Entre otros…

  • Permite el uso de rutas relativas en vez de absolutas: vital para reproducibilidad.

Proyectos

¿Por qué usar un proyecto de RStudio?

Proyectos en R: Ambiente Seguro y Transportable

Antes de usar R, cree un proyeto


  • Paso 1: Cree una carpeta de trabajo .

    • En donde quiera. Sea ordenado.

  • Paso 2: Abra RStudio .

  • Paso 3: Cree un proyecto y vincule el proyecto con la carpeta nueva.

    • A partir de este momento, la carpeta que contiene el archivo .Rproj es denominada carpeta raíz.

    • Todo lo que ocurra en R partirá de la carpeta raíz en adelante. Cree una estructura de carpetas.

  • Paso 3: Comience a trabajar. Puede usar R scripts o R Markdown / Quarto.

    • Nosotros usaremos Quarto, previa familiarización con R script.

Nuestro turno


  • Cree una carpeta de trabajo en Documentos, llámela mi_primer_proyecto.
  • Cree un proyecto de Rstudio y ubíquelo en la carpeta nueva mi_primer_proyecto.
  • Cree el primer script de R, anote lo siguiente: "# Este es mi primer script de R" y guárdelo como mi_primer_script.r.
  • Cree el primer archivo .qmd de Quarto, anote lo siguiente: "# Este es mi primer archivo quarto" y guárdelo como mi_primer_quarto.qmd.
  • Renderice el primer archivo quarto creado: mi_primer_quarto.qmd.
  • Verifique que los archivos han sido creados y se encuentran en la carpeta raíz mi_primer_proyecto.




05:00

Al igual que Stata, R es una calculadora

display 5 + 8

5 + 8

  • Suma:
3 + 8
[1] 11
  • Resta:
3 - 8
[1] -5
  • Multiplicación
3 * 8
[1] 24
  • División
3 * 8
[1] 24
  • Potencia:
3 ^ 3 # ^ se obtiene mediante: ALT + 94
[1] 27
  • Raíz cuadrada:
sqrt(16)
[1] 4
  • Otra raíz
8 ^ (1 / 3) # Raíz cúbica
[1] 2
  • Operación combinada
15 + (64 - 60) ^ (1/2)
[1] 17
  • Logaritmo natural:
log(12) # log es logaritmo natural o neperiano
[1] 2.484907
  • Logaritmo base 10:
log10(100)
[1] 2
  • Exponencial
exp(2)
[1] 7.389056
  • pi
pi
[1] 3.141593
  • Mayor que:
5 > 4
[1] TRUE
  • Menor igual que:
6 <= 3
[1] FALSE
  • Igualdad:
7 == 7
[1] TRUE
  • Diferente:
8 != 5
[1] TRUE
  • Si tu teclado puede usar ASCII, puedes usar los siguientes atajos:
Atajo Símbolo Significado Operador
Alt + 94 ^ … elevado a la potencia … Aritmético
Alt + 126 ~ … en relación a … Aritmético
Alt + 38 & … Y … Booleano
Alt + 124 | … O … Booleano
  • Si tu teclado no tiene ASCII, sugiero configurar teclado para que coincida con el idioma: Tutorial 1 y Tutorial 2.

Nuestro turno

  • En el script previamente creado mi_primer_script.r, escriba las siguientes operaciones matemáticas: cinco más ocho; tres por 23; veinte elevado al cubo; setenta y tres entre cinco. Ejecute el código y mire el resultado en la consola.

  • Copie las operaciones matemáticas escritas en mi_primer_script.r sobre mi_primer_quarto.qmd.

  • El código debe colocarse como chunk de código par que pueda ejecutarse. Ejecute el código de cada chunk.

  • Compare las salidas del mi_primer_script.r y de mi_primer_quarto.qmd.

  • ¿El símbolo # se ejecuta igual en un script de R y en un quarto markdown? ¿Cuál es la diferencia?

10:00

R es más que una simple calculadora

  • Pero…, al igual que Stata, R es más que solo una calculadora…

  • R es un lenguaje compuesto por “palabras”.

  • Las palabras son objetos en R:

    • Todo (casi) es un objeto.

  • Los verbos de R son las funciones:

    • Ejecutan acciones sobre otros objetos.

    • Los objetos sobre los que recae la acción se incluyen entre paréntesis.

Funciones en R

  • Estructura general de una función:
ejecuta_accion(sobre_esto)
ejecuta_accion(sobre_esto, hacia_eso, con_esto)


  • Dos funciones usuales son install.packages y library, las cuales instalan y cargan un paquete, respectivamente:
install.packages("package_name")
library(package_name)

Ayuda en R

  • La documentación acerca de una función puede ser consultada mediante ?
?library

Cargar conjunto de datos de R

  • Se pueden importar de diversas fuentes: MS Excel, .csv, .txt, Stata, SAS, SPSS, etc.

  • Algunos paquetes cuentan también con datos en formato de R: *.RData o .rds.

  • Importar datos de paquetes de R:
library(medicaldata)
data(covid_testing)
  • Consultar por descripción de los datos:
?covid_testing
  • Ver un resumen de los datos
head(covid_testing)
# A tibble: 6 × 17
  subject_id fake_…¹ fake_…² gender pan_day test_id clini…³ result demo_…⁴   age
       <dbl> <chr>   <chr>   <chr>    <dbl> <chr>   <chr>   <chr>  <chr>   <dbl>
1       1412 jhezane wester… female       4 covid   inpati… negat… patient   0  
2        533 penny   targar… female       7 covid   clinic… negat… patient   0  
3       9134 grunt   rivers  male         7 covid   clinic… negat… patient   0.8
4       8518 melisa… swyft   female       8 covid   clinic… negat… patient   0.8
5       8967 rolley  karsta… male         8 covid   emerge… negat… patient   0.8
6      11048 megga   karsta… female       8 covid   oncolo… negat… patient   0.8
# … with 7 more variables: drive_thru_ind <dbl>, ct_result <dbl>,
#   orderset <dbl>, payor_group <chr>, patient_class <chr>, col_rec_tat <dbl>,
#   rec_ver_tat <dbl>, and abbreviated variable names ¹​fake_first_name,
#   ²​fake_last_name, ³​clinic_name, ⁴​demo_group

Operador esenciales de R base

  • En un conjunto de datos, permite seleccionar la columna de interés.
  • Por ejemplo, género de los participantes:
covid_testing$gender
    [1] "female" "female" "male"   "female" "male"   "female" "male"   "female"
    [9] "male"   "male"   "male"   "female" "female" "male"   "female" "female"
   [17] "male"   "male"   "female" "female" "female" "male"   "female" "male"  
   [25] "female" "male"   "female" "male"   "female" "female" "male"   "female"
   [33] "female" "female" "female" "female" "male"   "female" "male"   "female"
   [41] "female" "male"   "male"   "female" "female" "male"   "male"   "male"  
   [49] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
   [57] "female" "female" "female" "female" "male"   "male"   "male"   "female"
   [65] "female" "female" "male"   "female" "male"   "female" "male"   "male"  
   [73] "female" "female" "female" "male"   "male"   "male"   "female" "male"  
   [81] "female" "female" "female" "female" "female" "male"   "female" "male"  
   [89] "male"   "male"   "male"   "female" "male"   "male"   "female" "female"
   [97] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
  [105] "female" "female" "male"   "male"   "female" "male"   "male"   "male"  
  [113] "female" "female" "male"   "male"   "male"   "female" "female" "male"  
  [121] "female" "female" "male"   "male"   "female" "female" "female" "male"  
  [129] "female" "female" "male"   "female" "female" "female" "female" "male"  
  [137] "male"   "male"   "male"   "male"   "male"   "female" "female" "male"  
  [145] "male"   "female" "male"   "female" "female" "male"   "male"   "male"  
  [153] "female" "female" "female" "male"   "female" "male"   "male"   "female"
  [161] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
  [169] "female" "male"   "female" "female" "female" "male"   "female" "male"  
  [177] "female" "female" "male"   "male"   "female" "female" "female" "female"
  [185] "female" "male"   "male"   "male"   "female" "male"   "male"   "female"
  [193] "female" "male"   "male"   "male"   "male"   "male"   "male"   "female"
  [201] "female" "female" "female" "female" "male"   "male"   "male"   "male"  
  [209] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
  [217] "male"   "female" "male"   "male"   "male"   "female" "male"   "male"  
  [225] "male"   "male"   "female" "female" "female" "female" "female" "male"  
  [233] "female" "male"   "male"   "female" "female" "male"   "female" "female"
  [241] "female" "male"   "female" "female" "male"   "male"   "female" "male"  
  [249] "female" "female" "female" "male"   "female" "female" "female" "male"  
  [257] "male"   "female" "male"   "female" "male"   "female" "male"   "male"  
  [265] "female" "female" "male"   "male"   "female" "female" "male"   "male"  
  [273] "female" "male"   "male"   "male"   "male"   "male"   "male"   "male"  
  [281] "female" "female" "male"   "female" "female" "female" "female" "female"
  [289] "female" "female" "male"   "female" "female" "male"   "male"   "female"
  [297] "female" "female" "female" "female" "male"   "female" "male"   "female"
  [305] "female" "male"   "male"   "male"   "male"   "female" "male"   "female"
  [313] "male"   "female" "male"   "male"   "female" "female" "male"   "female"
  [321] "female" "female" "female" "female" "male"   "male"   "male"   "female"
  [329] "female" "male"   "male"   "female" "female" "female" "male"   "male"  
  [337] "female" "male"   "female" "female" "male"   "male"   "male"   "male"  
  [345] "female" "male"   "male"   "male"   "female" "male"   "female" "female"
  [353] "female" "female" "female" "female" "female" "male"   "female" "male"  
  [361] "male"   "male"   "male"   "female" "male"   "male"   "female" "male"  
  [369] "female" "female" "female" "male"   "female" "female" "male"   "male"  
  [377] "female" "female" "female" "male"   "male"   "male"   "male"   "female"
  [385] "male"   "female" "male"   "female" "female" "male"   "female" "male"  
  [393] "female" "female" "male"   "female" "female" "male"   "female" "female"
  [401] "female" "female" "male"   "male"   "male"   "female" "female" "male"  
  [409] "male"   "female" "male"   "female" "male"   "male"   "male"   "female"
  [417] "female" "male"   "male"   "male"   "female" "male"   "female" "male"  
  [425] "female" "male"   "female" "female" "male"   "female" "female" "female"
  [433] "male"   "male"   "male"   "male"   "male"   "male"   "male"   "male"  
  [441] "male"   "male"   "male"   "female" "female" "male"   "male"   "male"  
  [449] "male"   "male"   "male"   "female" "male"   "female" "female" "male"  
  [457] "female" "female" "male"   "female" "male"   "male"   "male"   "female"
  [465] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
  [473] "female" "male"   "male"   "female" "female" "female" "female" "female"
  [481] "female" "female" "female" "female" "male"   "female" "female" "female"
  [489] "female" "female" "female" "female" "female" "male"   "male"   "female"
  [497] "female" "male"   "male"   "male"   "female" "female" "male"   "male"  
  [505] "female" "male"   "male"   "male"   "male"   "female" "male"   "female"
  [513] "female" "female" "female" "female" "female" "male"   "female" "male"  
  [521] "female" "female" "female" "female" "female" "male"   "female" "female"
  [529] "female" "female" "female" "female" "female" "male"   "male"   "male"  
  [537] "female" "male"   "female" "male"   "female" "female" "male"   "female"
  [545] "male"   "male"   "female" "female" "female" "male"   "male"   "female"
  [553] "male"   "male"   "female" "female" "male"   "male"   "male"   "male"  
  [561] "male"   "female" "female" "female" "male"   "female" "female" "female"
  [569] "male"   "female" "female" "male"   "male"   "female" "female" "female"
  [577] "female" "female" "female" "female" "male"   "male"   "female" "male"  
  [585] "female" "female" "female" "male"   "female" "female" "female" "female"
  [593] "female" "female" "male"   "female" "female" "female" "male"   "male"  
  [601] "female" "female" "male"   "female" "male"   "female" "male"   "female"
  [609] "female" "female" "female" "female" "female" "male"   "male"   "male"  
  [617] "female" "female" "female" "female" "male"   "male"   "female" "female"
  [625] "male"   "male"   "male"   "female" "female" "female" "female" "male"  
  [633] "female" "female" "female" "male"   "male"   "male"   "male"   "female"
  [641] "male"   "male"   "male"   "female" "male"   "male"   "male"   "female"
  [649] "male"   "male"   "male"   "male"   "female" "male"   "male"   "female"
  [657] "male"   "male"   "male"   "female" "male"   "male"   "female" "male"  
  [665] "female" "male"   "male"   "female" "male"   "female" "female" "male"  
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[14025] "female" "male"   "female" "female" "female" "female" "female" "female"
[14033] "male"   "female" "male"   "male"   "female" "male"   "female" "female"
[14041] "female" "male"   "female" "female" "male"   "female" "male"   "male"  
[14049] "female" "male"   "male"   "male"   "male"   "female" "male"   "female"
[14057] "female" "male"   "female" "male"   "female" "female" "male"   "female"
[14065] "female" "female" "female" "female" "female" "male"   "female" "female"
[14073] "female" "male"   "male"   "female" "female" "male"   "male"   "female"
[14081] "male"   "female" "male"   "female" "female" "female" "male"   "female"
[14089] "male"   "male"   "female" "male"   "male"   "female" "female" "male"  
[14097] "female" "female" "male"   "female" "female" "male"   "female" "male"  
[14105] "female" "female" "female" "female" "male"   "male"   "male"   "female"
[14113] "female" "male"   "female" "male"   "female" "female" "female" "female"
[14121] "female" "male"   "female" "female" "male"   "male"   "male"   "female"
[14129] "male"   "male"   "female" "female" "male"   "female" "female" "female"
[14137] "female" "male"   "female" "female" "female" "female" "male"   "female"
[14145] "female" "male"   "female" "female" "female" "female" "female" "male"  
[14153] "male"   "male"   "male"   "female" "female" "female" "male"   "female"
[14161] "female" "female" "female" "female" "female" "male"   "female" "female"
[14169] "male"   "female" "female" "female" "male"   "male"   "female" "male"  
[14177] "male"   "female" "male"   "male"   "female" "male"   "male"   "female"
[14185] "male"   "female" "male"   "male"   "male"   "female" "female" "female"
[14193] "female" "female" "male"   "male"   "male"   "male"   "male"   "female"
[14201] "female" "male"   "female" "female" "male"   "male"   "female" "male"  
[14209] "male"   "female" "female" "male"   "male"   "male"   "male"   "male"  
[14217] "male"   "female" "female" "male"   "male"   "male"   "male"   "male"  
[14225] "female" "female" "female" "female" "female" "male"   "male"   "female"
[14233] "male"   "male"   "female" "female" "female" "female" "female" "male"  
[14241] "female" "male"   "female" "male"   "male"   "female" "female" "male"  
[14249] "female" "female" "male"   "female" "male"   "male"   "female" "male"  
[14257] "male"   "male"   "male"   "female" "female" "male"   "male"   "male"  
[14265] "female" "female" "female" "female" "female" "male"   "male"   "female"
[14273] "female" "female" "female" "male"   "male"   "male"   "female" "male"  
[14281] "male"   "male"   "female" "male"   "female" "female" "female" "female"
[14289] "female" "female" "female" "female" "male"   "female" "female" "female"
[14297] "female" "female" "female" "male"   "female" "male"   "male"   "female"
[14305] "female" "female" "male"   "male"   "female" "female" "male"   "female"
[14313] "female" "female" "female" "male"   "female" "male"   "male"   "female"
[14321] "female" "male"   "female" "male"   "male"   "male"   "male"   "female"
[14329] "female" "male"   "male"   "female" "female" "female" "male"   "female"
[14337] "female" "female" "female" "male"   "female" "female" "male"   "female"
[14345] "male"   "female" "female" "female" "female" "female" "female" "female"
[14353] "male"   "female" "female" "female" "male"   "female" "female" "male"  
[14361] "female" "female" "female" "male"   "male"   "male"   "male"   "male"  
[14369] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
[14377] "female" "female" "female" "male"   "male"   "female" "male"   "male"  
[14385] "male"   "male"   "male"   "female" "female" "female" "male"   "male"  
[14393] "female" "male"   "female" "male"   "female" "male"   "female" "male"  
[14401] "female" "female" "female" "female" "male"   "male"   "male"   "male"  
[14409] "male"   "female" "male"   "female" "female" "male"   "male"   "male"  
[14417] "female" "female" "male"   "female" "male"   "female" "female" "female"
[14425] "female" "female" "male"   "male"   "male"   "male"   "male"   "male"  
[14433] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
[14441] "female" "female" "male"   "male"   "female" "male"   "female" "female"
[14449] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
[14457] "female" "female" "female" "male"   "male"   "male"   "female" "male"  
[14465] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
[14473] "female" "female" "male"   "female" "male"   "male"   "male"   "female"
[14481] "male"   "female" "female" "male"   "male"   "male"   "female" "female"
[14489] "male"   "male"   "female" "male"   "female" "male"   "male"   "female"
[14497] "male"   "male"   "female" "female" "female" "male"   "male"   "female"
[14505] "female" "female" "male"   "male"   "male"   "female" "female" "female"
[14513] "male"   "male"   "male"   "female" "male"   "male"   "male"   "female"
[14521] "female" "female" "female" "female" "male"   "male"   "male"   "male"  
[14529] "female" "male"   "male"   "female" "female" "male"   "male"   "male"  
[14537] "male"   "female" "female" "male"   "male"   "female" "female" "male"  
[14545] "female" "male"   "female" "female" "male"   "male"   "male"   "male"  
[14553] "female" "female" "female" "male"   "male"   "female" "male"   "male"  
[14561] "female" "female" "female" "male"   "male"   "male"   "male"   "male"  
[14569] "male"   "female" "male"   "male"   "female" "male"   "male"   "female"
[14577] "male"   "female" "female" "male"   "female" "male"   "female" "female"
[14585] "male"   "male"   "female" "female" "female" "female" "female" "male"  
[14593] "male"   "male"   "female" "female" "female" "male"   "female" "female"
[14601] "female" "female" "female" "female" "female" "male"   "male"   "female"
[14609] "female" "male"   "female" "female" "female" "female" "female" "male"  
[14617] "male"   "female" "female" "female" "male"   "male"   "male"   "female"
[14625] "female" "female" "male"   "female" "male"   "female" "male"   "female"
[14633] "male"   "female" "male"   "female" "male"   "male"   "female" "female"
[14641] "female" "female" "male"   "female" "male"   "male"   "female" "female"
[14649] "female" "female" "male"   "female" "male"   "male"   "female" "male"  
[14657] "female" "male"   "male"   "male"   "female" "female" "female" "male"  
[14665] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[14673] "female" "female" "male"   "female" "male"   "female" "female" "female"
[14681] "female" "female" "male"   "female" "male"   "female" "male"   "male"  
[14689] "female" "female" "female" "male"   "female" "female" "female" "female"
[14697] "female" "male"   "female" "female" "male"   "female" "male"   "female"
[14705] "female" "female" "male"   "female" "female" "male"   "female" "female"
[14713] "male"   "female" "female" "female" "male"   "female" "female" "female"
[14721] "female" "female" "male"   "female" "female" "female" "male"   "male"  
[14729] "male"   "male"   "male"   "male"   "female" "female" "male"   "male"  
[14737] "female" "female" "female" "male"   "male"   "female" "female" "female"
[14745] "male"   "male"   "female" "male"   "male"   "female" "male"   "male"  
[14753] "female" "female" "female" "female" "female" "female" "female" "male"  
[14761] "female" "female" "female" "female" "male"   "male"   "male"   "male"  
[14769] "male"   "male"   "female" "female" "male"   "female" "female" "male"  
[14777] "female" "male"   "female" "male"   "male"   "female" "female" "male"  
[14785] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
[14793] "female" "female" "female" "female" "female" "female" "male"   "female"
[14801] "male"   "male"   "female" "female" "female" "female" "female" "female"
[14809] "female" "male"   "male"   "female" "male"   "male"   "female" "female"
[14817] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
[14825] "female" "male"   "male"   "male"   "male"   "male"   "male"   "male"  
[14833] "male"   "female" "female" "female" "male"   "male"   "male"   "female"
[14841] "male"   "female" "male"   "male"   "female" "female" "female" "male"  
[14849] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[14857] "male"   "male"   "female" "male"   "male"   "female" "female" "male"  
[14865] "male"   "male"   "female" "female" "male"   "male"   "female" "female"
[14873] "female" "female" "female" "female" "male"   "female" "female" "female"
[14881] "female" "female" "female" "female" "female" "male"   "female" "female"
[14889] "male"   "male"   "female" "female" "male"   "female" "female" "female"
[14897] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
[14905] "male"   "male"   "female" "male"   "female" "female" "male"   "female"
[14913] "female" "male"   "female" "female" "male"   "female" "male"   "male"  
[14921] "male"   "female" "male"   "female" "male"   "female" "male"   "female"
[14929] "female" "female" "male"   "male"   "female" "male"   "male"   "female"
[14937] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
[14945] "female" "male"   "male"   "female" "male"   "male"   "female" "female"
[14953] "male"   "female" "male"   "female" "male"   "male"   "female" "male"  
[14961] "male"   "female" "female" "male"   "female" "male"   "female" "male"  
[14969] "male"   "male"   "female" "male"   "female" "female" "male"   "female"
[14977] "female" "female" "female" "female" "male"   "male"   "male"   "female"
[14985] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
[14993] "male"   "female" "male"   "female" "female" "female" "female" "female"
[15001] "male"   "male"   "female" "female" "female" "female" "female" "female"
[15009] "female" "male"   "female" "male"   "female" "male"   "female" "female"
[15017] "male"   "male"   "female" "male"   "male"   "female" "male"   "male"  
[15025] "female" "female" "male"   "female" "male"   "male"   "female" "female"
[15033] "female" "male"   "female" "female" "female" "female" "male"   "male"  
[15041] "male"   "female" "female" "female" "female" "male"   "female" "male"  
[15049] "male"   "male"   "male"   "male"   "female" "male"   "male"   "male"  
[15057] "male"   "female" "female" "female" "female" "female" "male"   "female"
[15065] "male"   "female" "male"   "male"   "male"   "female" "male"   "male"  
[15073] "male"   "male"   "female" "male"   "male"   "male"   "male"   "male"  
[15081] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
[15089] "female" "female" "female" "female" "female" "female" "male"   "female"
[15097] "female" "female" "male"   "male"   "male"   "male"   "female" "male"  
[15105] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
[15113] "female" "female" "female" "female" "male"   "female" "male"   "male"  
[15121] "male"   "male"   "female" "male"   "female" "female" "male"   "male"  
[15129] "male"   "male"   "female" "female" "female" "female" "male"   "female"
[15137] "male"   "female" "male"   "female" "male"   "male"   "male"   "male"  
[15145] "female" "female" "male"   "female" "female" "female" "female" "male"  
[15153] "male"   "female" "female" "male"   "female" "male"   "male"   "male"  
[15161] "male"   "male"   "female" "male"   "male"   "female" "female" "female"
[15169] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[15177] "male"   "female" "male"   "female" "male"   "male"   "female" "female"
[15185] "female" "male"   "male"   "female" "female" "female" "male"   "female"
[15193] "male"   "male"   "female" "female" "female" "male"   "male"   "male"  
[15201] "male"   "female" "male"   "female" "female" "female" "male"   "female"
[15209] "male"   "male"   "male"   "male"   "female" "male"   "male"   "female"
[15217] "male"   "female" "female" "female" "male"   "female" "female" "female"
[15225] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
[15233] "male"   "female" "male"   "male"   "male"   "female" "male"   "male"  
[15241] "male"   "male"   "female" "female" "male"   "female" "female" "female"
[15249] "male"   "female" "female" "male"   "male"   "male"   "female" "female"
[15257] "male"   "female" "female" "male"   "female" "female" "female" "male"  
[15265] "male"   "male"   "male"   "female" "male"   "male"   "male"   "female"
[15273] "male"   "female" "male"   "female" "female" "male"   "female" "female"
[15281] "female" "female" "female" "male"   "female" "female" "male"   "male"  
[15289] "male"   "female" "female" "male"   "female" "female" "male"   "male"  
[15297] "female" "female" "male"   "male"   "male"   "female" "male"   "male"  
[15305] "male"   "female" "male"   "male"   "female" "female" "male"   "female"
[15313] "male"   "male"   "female" "male"   "female" "female" "female" "female"
[15321] "male"   "female" "male"   "male"   "female" "female" "female" "male"  
[15329] "female" "male"   "female" "female" "female" "female" "female" "female"
[15337] "male"   "male"   "female" "female" "female" "male"   "male"   "male"  
[15345] "male"   "female" "female" "male"   "male"   "female" "female" "female"
[15353] "male"   "male"   "male"   "male"   "female" "male"   "female" "male"  
[15361] "female" "female" "male"   "female" "female" "male"   "male"   "male"  
[15369] "female" "female" "female" "female" "male"   "male"   "male"   "female"
[15377] "male"   "male"   "male"   "female" "male"   "female" "male"   "male"  
[15385] "male"   "male"   "female" "female" "female" "female" "male"   "male"  
[15393] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[15401] "female" "male"   "female" "male"   "male"   "female" "male"   "female"
[15409] "female" "male"   "male"   "female" "male"   "male"   "male"   "female"
[15417] "male"   "female" "female" "male"   "male"   "female" "female" "female"
[15425] "female" "female" "female" "male"   "female" "male"   "male"   "female"
[15433] "male"   "male"   "female" "female" "male"   "male"   "male"   "male"  
[15441] "female" "female" "male"   "female" "female" "male"   "female" "female"
[15449] "male"   "female" "male"   "female" "female" "male"   "male"   "male"  
[15457] "male"   "male"   "male"   "female" "male"   "female" "male"   "male"  
[15465] "male"   "male"   "male"   "female" "female" "female" "female" "female"
[15473] "female" "female" "female" "male"   "female" "male"   "male"   "male"  
[15481] "female" "female" "male"   "female" "male"   "female" "male"   "female"
[15489] "female" "female" "male"   "female" "female" "female" "male"   "male"  
[15497] "male"   "female" "male"   "male"   "female" "female" "male"   "female"
[15505] "male"   "male"   "female" "female" "male"   "male"   "female" "female"
[15513] "male"   "female" "male"   "male"   "male"   "female" "female" "female"
[15521] "female" "female" "male"   "female"
  • O la edad:
covid_testing$age
    [1]   0.0   0.0   0.8   0.8   0.8   0.8   0.8   0.0   0.0   0.9   0.9  18.0
   [13]   0.9   0.0  21.0  19.0   0.9  18.0   0.9   0.9  18.0   0.9  20.0  18.0
   [25]   0.0  19.0  18.0   0.9   0.9  18.0   0.0   1.0   1.0  21.0  18.0   0.9
   [37]  21.0   1.0  18.0   0.0  22.0   0.9   0.9   1.0  22.0  21.0   1.0   1.0
   [49]   1.0   0.0  18.0   1.0  21.0   1.0  22.0  18.0   1.0   1.0   1.0   1.0
   [61]   1.0  21.0   0.0  18.0   1.0  18.0  18.0   1.0   4.0   1.0   1.0   1.0
   [73]   5.0   1.0  22.0   1.0   0.0   1.0   1.0   1.0   1.0   1.0   5.0   1.0
   [85]   1.0   1.0   0.8  22.0  18.0  18.0  18.0   0.1   1.0  13.0  18.0   1.0
   [97]   1.0  19.0   1.0   1.0   0.1  22.0  18.0   0.0   1.0  18.0  18.0   1.0
  [109]  22.0   1.0   1.0  22.0  18.0   1.0  18.0   1.0  22.0   1.0  28.0   1.0
  [121]   1.0   1.0   1.0   1.0   1.0  19.0  23.0   1.0  20.0   1.0  22.0   1.0
  [133]   1.0   0.0   1.0   1.0  18.0   1.0   1.0   1.0   1.0   1.0  23.0  23.0
  [145]  19.0   1.0   1.0   0.8   1.0  23.0   0.0   1.0  23.0   0.0   0.9   1.0
  [157]   1.0   1.0  20.0   1.0   0.0   0.7   1.0  23.0  23.0   1.0   1.0   1.0
  [169]   1.0  18.0  21.0   1.0  23.0   1.0  18.0  23.0   1.0  23.0  23.0  18.0
  [181]   1.0   0.0   1.0  18.0   1.0  23.0   0.0   1.0   1.0   1.0   1.0   4.0
  [193]  18.0   1.0   0.3   1.0  23.0   1.0   0.0  23.0  18.0   1.0   1.0   0.0
  [205]   1.0   1.0   1.0   1.0   1.0  23.0  23.0   1.0   1.0  18.0   1.0   1.0
  [217]   5.0   1.0  23.0   1.0   1.0   1.0   1.0   1.0   7.0   1.0   1.0   1.0
  [229]   1.0  23.0   1.0   2.0   1.0  23.0   1.0   1.0   1.0  21.0   1.0  18.0
  [241]   1.0   7.0   0.0  18.0   1.0   1.0   1.0   1.0  23.0   0.2   1.0   1.0
  [253]   1.0   1.0   0.0  23.0   0.4   0.0   1.0   1.0   1.0   1.0  18.0   1.0
  [265]   1.0   0.5   1.0   1.0   0.1   1.0   1.0   1.0   1.0   1.0   1.0  23.0
  [277]   1.0   0.0  23.0   0.0  23.0  23.0   1.0  21.0  21.0  23.0   1.0   1.0
  [289]   1.0   0.9   9.0  23.0   1.0   1.0   4.0   1.0   1.0   1.0   1.0   1.0
  [301]   1.0   0.2  23.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0  23.0  23.0
  [313]   1.0   1.0  24.0   0.1   1.0   1.0   1.0  23.0   1.0   1.0   1.0   0.0
  [325]   0.0  23.0   1.0   1.0   0.4   1.0  24.0   1.0   1.0  22.0   1.0   1.0
  [337]   1.0  24.0  23.0   1.0   1.0   1.0   1.0   1.0  24.0   1.0   1.0   1.0
  [349]   1.0   1.0  21.0   1.0   1.0   1.0   1.0   1.0  24.0   1.0   1.0   1.0
  [361]   1.0  24.0  24.0   1.0   0.0  18.0  18.0   1.0   1.0   0.0  18.0   1.0
  [373]  24.0   1.0   1.0   1.0   1.0  24.0  24.0   1.0   1.0  24.0  18.0   0.1
  [385]   0.2   1.0   1.0   1.0   1.0   1.0   1.0  18.0   1.0   0.0   1.0   1.0
  [397]   1.0   1.0   1.0  24.0   0.0  21.0   1.0   1.0   1.0   1.0   1.0  24.0
  [409]   1.0   1.0  18.0  24.0   0.0  24.0   1.0   0.7   1.0   0.0   1.0   1.0
  [421]   1.0   1.0   0.0  18.0   1.0   1.0  18.0   1.0   1.0   1.0   1.0   1.0
  [433]   0.0   1.0   1.0  24.0   0.0   1.0   4.0  22.0   0.0   1.0   1.0   1.0
  [445]   1.0   1.0   0.0  24.0  18.0  24.0  24.0  24.0   1.0   1.0   1.0   1.0
  [457]   1.0   0.5   0.0   1.0   1.0   1.0   1.0   0.1  24.0   1.0   1.0   1.0
  [469]   1.0   1.0   1.0  18.0   0.1   1.0   1.0   1.0   1.0   1.0  19.0   0.2
  [481]   1.0   1.0   1.0  10.0   1.0   1.0   0.0   1.0   0.7   0.0   0.4   0.0
  [493]  18.0   1.0  24.0   0.0  24.0   1.0   1.0  24.0   1.0   0.0   1.0   1.0
  [505]   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0  22.0
  [517]   1.0  24.0  18.0   0.2  12.0   0.0  24.0   0.3   1.0   0.0   0.0   1.0
  [529]  24.0   1.0   1.0  22.0   0.1   1.0   1.0  40.0   0.0   1.0   1.0   1.0
  [541]   1.0   1.0  18.0  24.0   1.0   1.0   1.0   1.0  31.0   1.0   1.0  18.0
  [553]   1.0  24.0   1.0   1.0   0.0  25.0  25.0  24.0   1.0   1.0   0.0  18.0
  [565]   1.0  22.0   1.0  24.0   1.0  24.0   0.0   1.0   1.0   1.0   1.0  18.0
  [577]   3.0   0.0  18.0   0.0  24.0  24.0   1.0   0.3   1.0   1.0   1.0  24.0
  [589]   0.1   1.0   1.0  24.0  24.0  24.0  24.0   1.0   0.8   1.0   1.0   1.0
  [601]   1.0  18.0   1.0  24.0  18.0   1.0   7.0   1.0   1.0   1.0   1.0   1.0
  [613]   0.0  25.0  25.0   1.0   1.0   0.0   0.0   1.0   1.0   1.0   1.0  18.0
  [625]   1.0  24.0   0.4   1.0   1.0  18.0   0.0   1.0   1.0   1.0   1.0   1.0
  [637]  24.0   1.0   1.0   1.0   0.1  25.0  24.0  24.0   1.0   1.0  18.0   0.0
  [649]   1.0   0.0   0.4  22.0  24.0  22.0   1.0   1.0   1.0   0.0   1.0  24.0
  [661]  18.0  18.0   1.0   1.0   1.0   1.0   1.0  24.0   1.0   1.0   1.0   1.0
  [673]   1.0   0.0   0.0   0.0  24.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0
  [685]   1.0   1.0   1.0   1.0  25.0   1.0   1.0   1.0  25.0  25.0  25.0   0.0
  [697]   0.1   1.0  25.0   1.0   5.0   1.0   1.0   0.0   1.0  22.0   0.0   1.0
  [709]   1.0   1.0   1.0   5.0   1.0  18.0   1.0   1.0   1.0   0.7   1.0   0.0
  [721]  22.0   1.0   1.0  25.0  25.0  23.0  25.0   1.0   1.0   1.0   1.0   0.0
  [733]  18.0   1.0   1.0   1.0   0.9   1.0   0.3   1.0   0.0   1.0  26.0  25.0
  [745]  25.0  25.0  18.0   5.0  17.0   1.0   5.0   1.0   1.0   1.0  18.0   1.0
  [757]   1.0   1.0   0.0   0.6   1.0   0.0   1.0   0.0   0.0   1.0   1.0   0.0
  [769]   1.0   1.0   0.0  14.0   1.0   1.0   1.0   0.0   1.0  25.0   1.0  24.0
  [781]   1.0   1.0   1.0  18.0   1.0  25.0  18.0  25.0   1.0   1.0   0.0   1.0
  [793]   1.0   0.0   1.0   1.0  25.0   1.0  25.0  18.0   1.0  18.0   1.0  25.0
  [805]   0.0   0.2   1.0   1.0   0.0  23.0   2.0   1.0  25.0  25.0   1.0  25.0
  [817]   1.0  18.0   1.0   1.0   1.0   1.0  25.0   1.0   1.0   1.0   1.0   1.0
  [829]   1.0  25.0  25.0   1.0  18.0   1.0   1.0   1.0   0.0   1.0   1.0  18.0
  [841]   0.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0  23.0   1.0   1.0   1.0
  [853]   1.0   0.0   1.0   1.0   2.0  25.0   1.0   1.0   1.0   1.0   0.0   0.0
  [865]   1.0   1.0  25.0   1.0   1.0   1.0   0.0   1.0   1.0  18.0   1.0  53.0
  [877]   1.0   1.0  18.0   1.0   1.0   1.0  25.0  18.0  26.0  23.0   1.0  26.0
  [889]   1.0   1.0   1.0   1.0   1.0  25.0   1.0   1.0   3.0   1.0   0.0   1.0
  [901]  18.0   1.0   1.0   1.0   1.0  25.0   0.5   1.0   1.0   1.0   1.0   0.0
  [913]   0.0   1.0  18.0  25.0   1.0   1.0   1.0   1.0   0.0  25.0  25.0   1.0
  [925]   1.0   1.0  23.0  18.0   1.0   1.0  18.0   1.0   0.7  25.0   1.0   1.0
  [937]   1.0   1.0   1.0  18.0  18.0   1.0   1.0   1.0   1.0   1.0   1.0   1.0
  [949]   0.0   1.0   1.0   1.0   0.0   1.0   1.0   1.0  25.0   0.0   1.0   0.6
  [961]   1.0   1.0   0.0   1.0   0.0   1.0  18.0  18.0   1.0   1.0  25.0  25.0
  [973]   1.0   1.0  25.0   0.0   0.0  25.0   1.0   1.0  15.0   1.0   1.0   2.0
  [985]   1.0   1.0  23.0  23.0  24.0  25.0   1.0   0.0  27.0   1.0   1.0  28.0
  [997]   1.0  18.0   1.0   5.0  25.0   1.0  25.0  25.0  25.0  18.0   0.0   1.0
 [1009]   1.0   1.0   1.0   1.0   0.2   1.0   0.1   1.0   1.0  18.0  23.0   1.0
 [1021]  25.0  18.0   0.0  24.0   1.0   1.0   1.0   1.0   1.0  18.0   1.0   0.0
 [1033]   1.0  29.0   1.0  25.0  25.0   1.0   1.0   1.0   1.0   1.0   1.0   0.5
 [1045]   1.0  25.0   1.0   1.0   1.0   0.0   0.7   2.0   1.0  25.0   1.0   1.0
 [1057]   0.5   1.0   0.0   1.0   1.0   1.0   0.0   1.0   1.0   0.0   1.0   2.0
 [1069]   0.0   2.0  25.0   1.0   0.0   0.0   2.0   0.0   0.0  18.0   1.0   1.0
 [1081]  25.0   0.8   1.0  25.0  25.0  26.0   0.0   1.0   9.0   2.0  18.0   2.0
 [1093]   2.0   1.0  18.0   2.0  18.0   2.0   2.0   2.0   2.0   2.0  25.0  18.0
 [1105]  25.0   2.0  10.0   1.0   2.0   1.0   2.0  18.0   2.0  26.0   1.0   1.0
 [1117]   0.0   2.0   1.0   1.0   2.0   2.0   2.0  18.0  25.0  18.0  25.0  26.0
 [1129]   2.0   0.0   2.0  24.0   1.0   1.0  26.0  25.0   0.0   2.0  26.0   1.0
 [1141]  18.0   2.0   2.0   2.0   2.0   2.0  25.0   1.0  26.0  18.0   1.0  24.0
 [1153]   0.0   0.0   1.0  25.0  25.0  18.0  18.0  29.0   1.0   1.0  25.0  25.0
 [1165]   0.8   1.0  26.0  24.0   0.0   1.0   1.0   1.0   1.0  20.0   2.0   1.0
 [1177]   1.0  25.0   1.0  22.0   1.0   2.0   2.0   2.0  25.0   2.0   1.0   2.0
 [1189]   0.0  26.0   2.0   2.0   2.0  18.0   1.0  26.0  26.0   2.0  24.0   1.0
 [1201]   0.1   0.0   0.0  18.0   2.0   1.0   1.0   1.0   1.0   2.0   1.0   0.0
 [1213]   2.0  26.0   0.0   1.0   0.9   2.0   1.0   1.0   1.0   1.0   0.0   0.0
 [1225]   1.0   2.0  18.0   2.0  26.0   2.0   0.6   1.0  26.0   0.1   1.0   2.0
 [1237]   2.0   1.0   0.0   1.0  26.0   0.0   1.0   1.0   1.0   0.0  26.0   1.0
 [1249]   2.0   2.0   1.0   1.0   2.0  27.0   2.0   1.0   2.0   1.0   2.0  18.0
 [1261]   0.0   0.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0   2.0   2.0   2.0
 [1273]   2.0   0.0   2.0   2.0   2.0   2.0   2.0   0.0   2.0  26.0   2.0   2.0
 [1285]   0.0   2.0   2.0   2.0   2.0   4.0   2.0   2.0  18.0   2.0   2.0   2.0
 [1297]  26.0  26.0   0.0   2.0   2.0  26.0   2.0   1.0  18.0   0.1  18.0   2.0
 [1309]  28.0  12.0   2.0  29.0   2.0   0.0   2.0   2.0  18.0   0.0   2.0  18.0
 [1321]  26.0   2.0   2.0   2.0  18.0   2.0   2.0   2.0   9.0   2.0   2.0   2.0
 [1333]   2.0   2.0   0.5   2.0   0.0   2.0   2.0   2.0   0.0   2.0   2.0   0.3
 [1345]   2.0   2.0   2.0  26.0  26.0   0.0   1.0   2.0  18.0   2.0   2.0   0.8
 [1357]   2.0  29.0  18.0  18.0   0.8  26.0   0.0   1.0   0.0   1.0   2.0   0.0
 [1369]   0.0   2.0   0.0   2.0   0.0   2.0  24.0  26.0  26.0   1.0   2.0   2.0
 [1381]  26.0   1.0  26.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   0.2   2.0
 [1393]  18.0  26.0   0.0  26.0   2.0   0.0   0.0   0.0   2.0   2.0  29.0   2.0
 [1405]   2.0   2.0   2.0   2.0   0.0   0.0   2.0   0.0   2.0   2.0   0.7   0.0
 [1417]  26.0  30.0   2.0   0.0   2.0   0.0  18.0   2.0   0.0   0.0   2.0  26.0
 [1429]   2.0   0.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   0.3   0.1
 [1441]   2.0   2.0   2.0   0.0   1.0   2.0  26.0   2.0   2.0  26.0  26.0   2.0
 [1453]   2.0   2.0   2.0  18.0   2.0   2.0   0.6  24.0  30.0  26.0   0.0  26.0
 [1465]  18.0   2.0   0.0   2.0  18.0   2.0  31.0   1.0   2.0   1.0  18.0   2.0
 [1477]   2.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0
 [1489]  26.0   0.0   2.0  18.0   2.0   2.0   0.1   2.0   2.0  18.0   1.0   1.0
 [1501]   2.0   2.0   2.0   2.0   2.0  31.0   1.0   0.0   2.0  18.0   2.0   2.0
 [1513]   1.0   2.0   2.0  16.0   2.0  31.0   0.0   2.0   2.0   2.0   2.0  24.0
 [1525]  18.0  18.0   0.0   0.0   2.0   2.0   2.0  18.0   2.0   0.0   2.0   2.0
 [1537]   2.0   2.0   0.0  18.0   1.0   0.2  18.0   1.0   2.0  18.0  26.0   2.0
 [1549]   0.7   2.0   2.0   2.0  18.0  26.0  26.0   2.0   0.0  18.0   0.0  18.0
 [1561]  26.0   2.0   2.0   2.0  26.0   0.0  26.0   2.0   1.0   2.0   2.0   2.0
 [1573]   2.0  26.0   0.0   0.0   2.0   0.0   2.0   2.0   2.0   2.0   2.0  18.0
 [1585]   2.0   0.0  27.0  27.0  27.0  26.0  24.0  26.0   2.0   2.0   2.0   2.0
 [1597]   2.0  28.0   0.7  26.0   2.0   2.0   2.0   2.0   0.2   2.0   2.0   2.0
 [1609]   2.0   2.0   2.0  26.0   0.9   2.0   0.1  18.0   2.0   2.0   1.0   0.0
 [1621]   2.0   0.7   2.0   2.0  26.0   2.0  26.0   2.0  26.0   2.0   2.0   0.2
 [1633]   2.0   2.0   2.0   2.0   2.0   2.0   2.0   0.0  26.0  18.0  18.0   2.0
 [1645]   2.0  35.0   2.0   2.0   2.0   2.0   2.0   2.0  26.0   0.0   0.7  26.0
 [1657]   1.0   2.0   2.0  91.0   0.1   2.0   2.0   0.1   2.0   2.0   2.0   2.0
 [1669]  18.0  18.0   2.0  15.0   2.0  26.0  26.0  18.0   2.0   2.0   2.0  25.0
 [1681]   2.0   0.3  18.0   0.1  26.0   0.5   2.0   2.0  26.0   2.0   1.0   2.0
 [1693]   2.0  26.0   2.0   0.1   2.0   2.0   2.0  26.0   2.0  26.0   2.0   1.0
 [1705]  18.0   0.6   2.0   2.0   2.0   2.0   2.0  26.0  18.0   2.0  26.0   1.0
 [1717]   1.0   2.0   2.0   2.0  18.0   2.0  26.0  18.0  26.0   2.0  26.0  18.0
 [1729]  18.0  26.0   2.0   2.0   2.0  18.0   0.3   2.0  18.0  26.0   2.0   0.0
 [1741]   2.0   0.6   2.0   2.0   2.0   0.0   2.0   2.0  91.0  27.0   1.0   1.0
 [1753]   2.0   2.0   0.9   2.0   2.0   2.0  12.0   2.0  31.0   2.0   1.0   0.1
 [1765]  28.0  26.0  27.0  26.0  27.0   2.0  18.0   2.0   2.0  18.0   2.0  26.0
 [1777]   2.0   2.0   2.0   2.0  27.0   2.0   2.0   2.0   0.1   2.0  18.0   1.0
 [1789]   2.0  27.0   2.0   2.0   2.0   0.1   2.0  27.0   0.0   2.0   2.0   2.0
 [1801]   2.0  26.0  18.0   2.0   2.0   2.0   0.3   2.0   2.0   2.0   2.0   2.0
 [1813]  18.0  10.0   0.0  27.0   2.0   2.0  18.0   2.0  18.0   1.0   0.1   0.1
 [1825]  18.0   2.0   2.0   2.0   0.9  25.0  18.0   2.0  24.0  18.0   2.0  18.0
 [1837]  18.0  27.0   2.0  18.0   2.0  27.0   2.0  26.0  26.0  18.0   2.0   2.0
 [1849]   2.0   0.0   2.0   2.0   2.0   2.0  26.0  26.0  18.0   2.0   2.0   2.0
 [1861]  18.0   2.0   2.0   3.0   0.6   2.0  26.0  18.0   2.0   0.0  18.0   2.0
 [1873]   2.0  27.0  26.0   2.0   2.0   1.0   2.0   2.0   2.0   2.0   0.0   2.0
 [1885]   2.0  27.0   2.0   0.7   2.0   0.0   2.0  18.0   2.0   3.0  26.0  50.0
 [1897]   2.0   2.0   2.0   2.0   2.0   0.3  27.0   0.1   3.0  18.0   2.0   2.0
 [1909]   0.5   2.0   2.0   1.0   2.0  18.0   2.0  27.0  27.0   0.1   2.0   2.0
 [1921]   2.0  27.0   0.0   1.0   2.0  27.0   2.0   2.0  31.0   0.0   2.0  27.0
 [1933]   2.0   2.0   2.0  27.0  14.0   1.0   0.2  18.0   2.0  18.0   2.0   2.0
 [1945]   0.7   1.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0   2.0   2.0  27.0
 [1957]  27.0  26.0  18.0   0.1   2.0   0.8   2.0   2.0   2.0   2.0  11.0   3.0
 [1969]   2.0   2.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0  27.0   2.0   2.0
 [1981]   2.0   2.0   2.0  18.0   3.0  18.0   2.0  18.0   2.0  27.0   0.1   2.0
 [1993]   1.0  27.0   2.0   2.0  26.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0
 [2005]  27.0   2.0  27.0   2.0  27.0  26.0   0.1  27.0   1.0   2.0   4.0  27.0
 [2017]  16.0   0.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0
 [2029]   2.0   2.0   2.0   2.0   2.0   2.0   0.0   2.0   0.1   2.0   2.0   2.0
 [2041]   2.0  27.0   2.0   0.0   2.0   2.0   0.6  27.0   2.0  18.0  18.0   3.0
 [2053]   0.1   2.0   2.0   2.0  27.0   2.0   2.0  18.0   2.0  32.0   0.1   2.0
 [2065]   2.0   2.0   0.2   3.0   3.0  27.0   2.0  27.0   2.0  27.0   2.0   2.0
 [2077]   2.0  12.0   2.0   2.0  18.0  27.0   2.0   0.2   0.5  27.0   1.0   2.0
 [2089]   2.0  12.0   2.0   2.0   2.0   2.0   2.0  18.0   2.0   0.1   2.0   2.0
 [2101]   2.0   5.0   2.0   2.0   2.0   7.0   1.0   2.0   2.0   2.0  27.0   2.0
 [2113]  27.0  27.0   2.0  18.0   2.0   2.0   2.0  28.0  27.0   2.0   3.0   0.1
 [2125]   0.1  27.0  27.0  27.0  27.0   2.0   2.0  18.0   2.0   2.0   2.0  18.0
 [2137]   0.7   2.0   2.0   0.1   2.0   2.0   2.0  27.0  27.0  27.0   2.0  60.0
 [2149]   2.0   2.0  27.0   2.0   2.0   2.0   2.0  18.0   0.1  18.0   2.0  27.0
 [2161]  25.0   2.0   2.0   2.0   2.0   2.0   0.9   2.0   2.0   2.0   2.0   2.0
 [2173]  25.0   2.0   3.0  27.0  18.0   2.0   0.5   2.0  18.0  13.0  27.0  28.0
 [2185]   1.0   0.1   2.0   2.0   2.0   2.0   2.0   2.0   2.0   2.0   0.6   2.0
 [2197]  27.0  18.0  12.0   2.0   2.0   2.0   2.0   2.0   2.0  27.0   2.0   2.0
 [2209]  18.0  18.0  42.0  27.0   2.0   2.0   2.0   4.0  28.0   0.1  27.0  27.0
 [2221]   2.0   2.0  32.0   0.7   2.0   2.0   0.1   2.0   2.0   2.0   2.0  12.0
 [2233]  25.0   2.0   2.0   0.3   2.0  18.0   0.3  32.0   2.0   2.0   2.0   2.0
 [2245]   2.0   0.0   3.0   1.0   2.0  27.0  27.0   1.0   2.0   2.0   1.0   2.0
 [2257]  18.0   2.0   1.0   2.0  29.0   2.0   2.0   0.6   0.6   0.1   1.0   2.0
 [2269]   2.0   2.0   2.0   2.0   0.2   0.1  18.0   2.0   2.0  18.0   0.1   2.0
 [2281]   2.0   2.0   2.0  13.0   2.0   0.2  28.0  27.0  18.0   0.1   2.0  27.0
 [2293]   2.0   2.0   2.0  32.0   2.0  18.0   2.0   2.0   2.0   2.0   2.0   2.0
 [2305]   4.0   2.0  27.0   2.0   2.0   2.0   2.0   2.0   2.0   0.1  18.0  29.0
 [2317]   0.1   2.0   2.0   2.0  27.0   0.1  18.0   0.7   2.0  32.0   2.0   0.1
 [2329]  18.0   4.0   0.1   2.0   2.0   2.0  27.0   1.0   2.0   0.1   0.1   2.0
 [2341]   0.1   2.0   0.2  18.0   2.0   2.0   2.0   2.0   2.0  27.0   2.0   2.0
 [2353]   2.0   2.0   2.0   0.1  18.0   0.0  27.0   0.0  27.0   0.1   1.0  27.0
 [2365]   2.0  18.0   2.0   2.0   1.0  18.0  27.0   0.2   0.1   6.0  18.0   1.0
 [2377]   2.0   2.0  27.0   2.0   0.1   2.0  29.0   2.0   2.0  18.0   2.0   3.0
 [2389]   2.0  27.0   1.0  27.0   2.0   2.0   1.0  18.0   2.0  18.0  27.0   2.0
 [2401]   2.0   5.0  18.0  20.0  27.0  18.0   2.0   2.0   1.0   2.0   5.0   2.0
 [2413]  18.0  33.0   2.0   2.0   0.1   4.0  27.0  15.0  35.0  28.0   2.0   0.1
 [2425]   2.0  18.0   0.1   2.0   2.0  18.0   2.0   2.0   0.0   2.0   1.0   0.2
 [2437]   1.0   0.9  28.0  27.0   2.0  28.0   2.0   0.0   2.0   2.0   0.1   2.0
 [2449]   2.0   1.0  18.0   2.0   0.1   2.0   2.0   2.0  33.0   2.0   0.1  28.0
 [2461]  18.0   0.1   0.1   2.0   2.0   2.0  28.0  28.0   0.1  18.0   2.0  27.0
 [2473]   2.0   2.0   2.0  18.0   0.1   2.0   2.0   0.1   2.0   2.0   2.0   2.0
 [2485]  18.0   0.2   2.0   2.0  28.0  28.0  18.0   2.0   1.0  28.0  18.0  28.0
 [2497]   2.0   2.0   2.0   0.1   2.0   2.0   0.1  27.0   2.0  18.0  28.0   0.5
 [2509]   0.1   2.0   0.1   2.0   2.0   2.0   2.0   2.0  27.0  28.0   0.1   0.1
 [2521]  27.0   0.4   2.0  18.0   2.0   2.0  18.0   2.0  28.0  28.0  28.0   2.0
 [2533]   0.1  18.0   2.0  18.0   2.0  33.0  18.0   2.0   2.0  18.0  28.0   2.0
 [2545]  18.0   2.0   2.0   2.0   2.0  30.0   2.0   2.0   2.0   2.0   2.0   4.0
 [2557]  12.0   2.0  28.0   2.0  15.0   2.0   2.0   2.0  30.0   2.0  91.0   0.1
 [2569]   2.0   0.1   2.0   2.0   1.0   2.0   2.0  18.0  18.0   2.0  31.0  27.0
 [2581]   2.0   2.0  27.0   0.1   2.0   2.0   2.0  18.0   2.0   2.0   0.1  25.0
 [2593]  27.0  28.0  28.0   2.0   2.0   2.0   2.0   0.1  28.0   2.0   0.1   2.0
 [2605]   0.0   2.0   2.0  18.0   2.0   2.0   2.0   2.0   1.0   2.0  28.0   3.0
 [2617]  28.0  28.0  29.0  28.0  28.0  30.0   2.0   2.0  28.0  18.0   2.0   2.0
 [2629]   2.0   2.0   2.0  28.0   2.0   2.0   0.0   0.1   2.0   0.1   0.1  28.0
 [2641]   2.0   2.0   2.0  91.0   2.0   0.7   0.0   2.0  28.0   2.0   1.0   2.0
 [2653]   0.1  28.0   2.0  28.0   2.0   3.0   3.0   2.0  13.0   1.0   2.0   0.1
 [2665]   0.2  18.0   3.0   0.3  13.0  18.0   0.1   2.0   3.0   3.0   1.0   3.0
 [2677]   0.1   0.8   0.1   2.0   3.0   0.1   3.0   3.0   0.1   1.0   1.0  28.0
 [2689]   0.1  28.0   3.0  28.0   2.0  28.0   3.0  18.0   2.0   3.0   1.0  18.0
 [2701]   3.0  28.0   3.0   0.1   3.0   3.0   3.0   0.0  33.0   0.1   3.0   3.0
 [2713]   3.0   3.0  18.0   2.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0   0.1
 [2725]   3.0   3.0   3.0  28.0   0.7   3.0   3.0   3.0  28.0   3.0   0.1   4.0
 [2737]   0.1  18.0  18.0   2.0   3.0  18.0   3.0   2.0   3.0   3.0   3.0   3.0
 [2749]   3.0   3.0   3.0   3.0   0.3  35.0  28.0  18.0  18.0   0.6   3.0  18.0
 [2761]   3.0  18.0   3.0   3.0   3.0  28.0   2.0   3.0   3.0   0.1   3.0   3.0
 [2773]   3.0   3.0   3.0   3.0  33.0   3.0  91.0   3.0   3.0   3.0  28.0  25.0
 [2785]   3.0  28.0   3.0  28.0  28.0  28.0   1.0   3.0   0.9   0.1  18.0   3.0
 [2797]   0.3   3.0   3.0   3.0  28.0   3.0   7.0   1.0   3.0  28.0  18.0  28.0
 [2809]   0.0  28.0   3.0  18.0   0.1   3.0   3.0   3.0   3.0   3.0   3.0   3.0
 [2821]   2.0   3.0   3.0  29.0  28.0   3.0   3.0   0.1   2.0   0.1   3.0   3.0
 [2833]  18.0   0.1  28.0   3.0  18.0  18.0   3.0   0.6   3.0   3.0   3.0   0.5
 [2845]  25.0  28.0  28.0   3.0   0.1   3.0   0.1   0.1  33.0   2.0   3.0   1.0
 [2857]   3.0  18.0   3.0   3.0   0.1   3.0  26.0  18.0   0.1   1.0  28.0   0.1
 [2869]  15.0   3.0  28.0   3.0   3.0   0.1  28.0   2.0   3.0   3.0   3.0  14.0
 [2881]   3.0  18.0   3.0   4.0  18.0  29.0  29.0  30.0   3.0  29.0  29.0   3.0
 [2893]   3.0   0.1   3.0   3.0   0.1  18.0   3.0   3.0   0.3   3.0   3.0  29.0
 [2905]   3.0   3.0   0.1   3.0  92.0   0.3  25.0   3.0   0.1   3.0   0.1   3.0
 [2917]   3.0  29.0   0.3   3.0   0.1   3.0   3.0   3.0   0.1   3.0  25.0  34.0
 [2929]  28.0  28.0   3.0   3.0  28.0   2.0  18.0   3.0   3.0   3.0  29.0   0.4
 [2941]   3.0   3.0   3.0   3.0   3.0   3.0   1.0   3.0   3.0   4.0   3.0   3.0
 [2953]   3.0  18.0   3.0   3.0   3.0  29.0   3.0  28.0   3.0   3.0   3.0  18.0
 [2965]   3.0   3.0   3.0   3.0   3.0   3.0   0.1   0.7   3.0   2.0   3.0   0.1
 [2977]   4.0   0.2   3.0   3.0   0.1  29.0   3.0  18.0   3.0   2.0   1.0   3.0
 [2989]   3.0  28.0   3.0   2.0   3.0  18.0  28.0   3.0   3.0   3.0  29.0  18.0
 [3001]  18.0  29.0  29.0  28.0  28.0   3.0  18.0   3.0   1.0   3.0  34.0   3.0
 [3013]  18.0  28.0  29.0   0.3   3.0   3.0   3.0   3.0  32.0   3.0   3.0   3.0
 [3025]   3.0   3.0  18.0   3.0   0.1   3.0   3.0   3.0  28.0  29.0   0.1   3.0
 [3037]   1.0   3.0  21.0   2.0   3.0   3.0   3.0   2.0   3.0   0.0  29.0   3.0
 [3049]   0.1   3.0  29.0   3.0  34.0   2.0   3.0   3.0  29.0   3.0   3.0   3.0
 [3061]   3.0  34.0   3.0   3.0  18.0   0.1  18.0   3.0   3.0   3.0   3.0  29.0
 [3073]   1.0   3.0  18.0   0.4   3.0   0.1   3.0   0.1  29.0  18.0   3.0   3.0
 [3085]   3.0  29.0  29.0  29.0  29.0   0.1  18.0   0.1  29.0   3.0   0.1   3.0
 [3097]  91.0   3.0   3.0   1.0  29.0  18.0  29.0   3.0   3.0   3.0   3.0   2.0
 [3109]   1.0  29.0   9.0   0.1   3.0   3.0   3.0   3.0   0.1   3.0   3.0   3.0
 [3121]   3.0   3.0   2.0  22.0   0.1   3.0   0.1   3.0  29.0   3.0   3.0   3.0
 [3133]   3.0  29.0   3.0   0.8  18.0   3.0   3.0   3.0  14.0   3.0  18.0   3.0
 [3145]   3.0   3.0   0.8  29.0  18.0   3.0   3.0   2.0   3.0   3.0   3.0   2.0
 [3157]   0.1   0.1   2.0   3.0   0.1   3.0   3.0  18.0  91.0  12.0  29.0   3.0
 [3169]  29.0   3.0   3.0   3.0  29.0  35.0  33.0  29.0   3.0   1.0   3.0  29.0
 [3181]   0.5   3.0  29.0  29.0   3.0   3.0   3.0   3.0   3.0  29.0  18.0   3.0
 [3193]   3.0  18.0   3.0  18.0   3.0  18.0   1.0  18.0   2.0   3.0   3.0  30.0
 [3205]  30.0  29.0   2.0   3.0   0.1   3.0   3.0   3.0   3.0  18.0   0.1   3.0
 [3217]   1.0   3.0   2.0   3.0   0.1  29.0   1.0   3.0  18.0   0.1   3.0   3.0
 [3229]   3.0   3.0  29.0   3.0   0.1  34.0   0.4  29.0   3.0  18.0   3.0   3.0
 [3241]   3.0  34.0   1.0  18.0   3.0   1.0  18.0   0.2   3.0   3.0  18.0  29.0
 [3253]  18.0   3.0   3.0   0.1   3.0  29.0  14.0  29.0  29.0   0.1   3.0   0.7
 [3265]  29.0   3.0  18.0   3.0   3.0   3.0   0.6   3.0   0.1   0.0   3.0   0.7
 [3277]   3.0   3.0   3.0   3.0   0.1   3.0  29.0  91.0   3.0   3.0   4.0  29.0
 [3289]  29.0  34.0   3.0   0.1  35.0   3.0   3.0  18.0   0.1  29.0   3.0   0.1
 [3301]  29.0   3.0   0.7   0.1   3.0   3.0   3.0   0.0   0.2  29.0   0.1  18.0
 [3313]   3.0   3.0   4.0  18.0   3.0   0.0   0.1  18.0  29.0   3.0   3.0   3.0
 [3325]   3.0   3.0   3.0   3.0  28.0  18.0  25.0  29.0   3.0   3.0   3.0   3.0
 [3337]   3.0   3.0  18.0   3.0  18.0  44.0   3.0   3.0   3.0   0.1  18.0  24.0
 [3349]   3.0   3.0  18.0   3.0  29.0   3.0   3.0   3.0  30.0  30.0  29.0  18.0
 [3361]   0.1   3.0   0.2   2.0   0.8   1.0   3.0  29.0   3.0  29.0   0.3   3.0
 [3373]  18.0   3.0   3.0   3.0  13.0   3.0   3.0   3.0   4.0   0.1   3.0   3.0
 [3385]   3.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0   4.0  29.0   1.0  29.0
 [3397]  29.0   1.0   0.2   3.0   3.0   3.0  29.0   0.2   0.1  29.0   3.0  18.0
 [3409]   3.0   1.0   0.3   2.0   0.1   1.0  30.0  29.0   3.0  36.0  18.0   3.0
 [3421]   3.0  13.0  29.0  30.0   3.0   0.1   3.0  18.0   3.0   0.6   3.0   3.0
 [3433]  12.0   4.0  19.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0  29.0   3.0
 [3445]   1.0   3.0   0.2  18.0   3.0   0.7  18.0   3.0   3.0   1.0   4.0   2.0
 [3457]  35.0   3.0   3.0   3.0   4.0   3.0   3.0  36.0   3.0  29.0  29.0  29.0
 [3469]   0.2   3.0   3.0   4.0  30.0  18.0  30.0  30.0  18.0   0.1   3.0   3.0
 [3481]   3.0   3.0  38.0  30.0  29.0   3.0  30.0  29.0  29.0   3.0  18.0   3.0
 [3493]   1.0   3.0  30.0   0.2   3.0   3.0   0.1   3.0   0.1   4.0  30.0   3.0
 [3505]   3.0  29.0   0.2   3.0   3.0   0.1   0.2  29.0   3.0  30.0   3.0   3.0
 [3517]   3.0  29.0   0.9   3.0  38.0  30.0   3.0   3.0   0.4  18.0   3.0  30.0
 [3529]   0.1   3.0   0.1   3.0   0.2   2.0   3.0   3.0  39.0   3.0  29.0   3.0
 [3541]   0.2  18.0   0.7  18.0   2.0  36.0   2.0   3.0   3.0   0.1  18.0   3.0
 [3553]   3.0  18.0   3.0  18.0   3.0  36.0   0.2  39.0   5.0   0.2   3.0   0.2
 [3565]   3.0  18.0   3.0   3.0   1.0   0.1   0.2   0.2   3.0   3.0  30.0   3.0
 [3577]   3.0  29.0   3.0  17.0   0.7   3.0   3.0   0.2   3.0  18.0   3.0  39.0
 [3589]   3.0   3.0   3.0   4.0   2.0  29.0  18.0   3.0   3.0   3.0   3.0   3.0
 [3601]   3.0   2.0   0.2   3.0   3.0  18.0  30.0  30.0   0.2  18.0  39.0   3.0
 [3613]   3.0   0.2   3.0  30.0   3.0   3.0   3.0   4.0   3.0  29.0  29.0   3.0
 [3625]   0.4   3.0   3.0   3.0  92.0   3.0   3.0  37.0   3.0  18.0  39.0  29.0
 [3637]   0.2   3.0   3.0   1.0   3.0   3.0   0.2   3.0   3.0  14.0   2.0  40.0
 [3649]  29.0  18.0   0.2   3.0   0.2   0.9   3.0   3.0   3.0  30.0   2.0  30.0
 [3661]   3.0   3.0   1.0   3.0  38.0  18.0   3.0   0.7   3.0   3.0   3.0   4.0
 [3673]  29.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0   3.0   5.0   3.0   3.0
 [3685]   3.0   3.0   0.5  30.0   3.0   0.2   3.0   3.0   3.0   0.2   3.0   3.0
 [3697]   3.0   3.0   3.0   3.0   5.0   3.0  30.0  30.0   2.0   3.0   3.0  35.0
 [3709]  10.0   3.0   3.0   3.0   0.2  18.0   0.2   3.0   3.0   3.0  30.0  18.0
 [3721]  30.0  30.0   3.0   1.0   3.0  18.0  18.0   3.0   3.0   3.0   3.0   3.0
 [3733]   3.0  18.0   5.0   5.0  30.0  31.0  31.0  30.0  30.0   3.0   4.0   0.4
 [3745]   2.0   3.0   0.2   2.0  30.0  30.0   2.0   3.0   3.0   4.0  18.0   4.0
 [3757]   0.2   2.0  18.0   4.0   4.0  18.0   0.9   1.0   4.0   4.0   4.0   4.0
 [3769]   4.0   4.0   4.0  18.0   4.0  30.0  30.0   4.0  18.0   0.2   4.0   4.0
 [3781]  18.0   4.0   1.0   4.0  30.0   4.0   4.0   4.0   4.0   4.0  18.0   0.7
 [3793]   4.0   0.6   4.0   4.0   4.0   4.0   3.0  30.0   4.0  30.0  18.0  18.0
 [3805]  14.0   4.0   5.0  15.0  30.0   4.0  30.0   0.0   4.0   0.2   4.0   4.0
 [3817]  30.0   4.0  40.0  30.0   0.2   0.2   4.0  30.0   4.0   4.0   4.0  25.0
 [3829]   4.0   4.0   0.2  30.0  18.0  18.0   4.0   2.0   4.0   4.0   4.0  16.0
 [3841]  30.0   4.0  31.0   4.0   1.0  30.0  30.0   0.2   4.0   4.0   4.0   4.0
 [3853]   0.2   4.0   0.2   4.0  18.0   4.0  41.0  30.0   4.0   0.2  38.0  18.0
 [3865]  31.0   4.0   4.0  30.0  15.0   4.0   4.0   0.2  18.0   0.6   1.0   0.2
 [3877]  30.0  30.0  30.0   4.0   4.0   3.0   3.0   0.2   4.0   4.0   4.0   5.0
 [3889]   4.0  30.0   4.0  18.0  18.0  25.0   5.0   4.0  30.0   4.0   0.9   4.0
 [3901]  25.0   4.0  30.0  30.0  31.0  30.0   4.0  30.0   4.0  30.0   4.0   4.0
 [3913]   2.0  30.0   4.0   4.0   4.0   4.0   4.0   4.0  30.0   4.0   4.0   4.0
 [3925]   4.0  30.0   4.0   4.0   4.0   5.0   5.0   4.0  30.0  30.0   4.0  30.0
 [3937]   1.0   0.0   4.0   0.2   4.0   4.0   4.0   4.0   1.0   4.0   4.0  41.0
 [3949]   0.2  37.0   4.0   0.2   4.0  18.0   4.0   4.0   4.0   0.2  30.0  18.0
 [3961]  18.0  30.0   4.0   4.0   4.0   4.0   4.0  30.0   3.0   4.0   4.0   4.0
 [3973]   1.0   0.5  26.0   4.0  30.0  39.0   4.0   0.2   4.0   0.2  30.0  30.0
 [3985]   3.0  18.0  41.0   4.0  30.0  30.0   0.2   4.0   4.0   4.0   4.0   4.0
 [3997]   0.2   4.0   4.0  30.0   4.0  18.0   4.0   0.2  30.0   4.0   0.2   1.0
 [4009]   0.2   4.0   4.0   2.0  18.0   4.0   4.0   0.5   4.0   4.0   4.0  30.0
 [4021]   2.0   4.0   4.0   4.0  30.0   4.0   4.0   4.0   4.0   4.0   2.0  30.0
 [4033]   4.0   4.0   1.0   4.0   0.2   0.2   4.0   4.0   2.0  18.0   4.0   4.0
 [4045]   0.2   4.0   3.0  30.0   4.0   0.2   4.0   4.0  18.0   4.0   5.0  15.0
 [4057]   3.0  31.0  31.0   4.0   4.0   4.0  31.0   0.2  18.0   4.0  30.0  31.0
 [4069]  31.0   4.0   2.0   4.0   4.0  39.0   4.0   1.0   4.0   4.0   1.0  91.0
 [4081]  31.0  24.0  31.0  25.0  32.0   4.0   0.2  12.0   0.2   4.0   0.2  18.0
 [4093]   4.0   4.0   4.0   4.0   4.0   4.0   4.0  18.0  31.0   4.0  31.0  18.0
 [4105]   4.0  18.0   0.2  18.0  31.0  31.0  18.0  42.0   0.0   4.0   4.0  18.0
 [4117]  25.0   4.0  30.0   0.2   4.0   4.0   4.0  31.0  30.0  30.0   0.2   1.0
 [4129]   4.0   4.0  31.0  30.0   4.0   4.0   4.0   4.0   4.0   4.0   4.0   4.0
 [4141]  18.0  21.0   0.2   4.0  18.0  42.0  13.0   7.0  24.0  31.0   4.0  30.0
 [4153]   4.0  39.0   4.0   4.0   0.2   4.0   4.0   4.0   0.2   0.0   4.0   4.0
 [4165]   4.0   4.0   4.0   4.0   4.0   2.0  31.0   4.0   4.0  32.0   1.0   4.0
 [4177]   4.0   4.0   0.5   4.0   4.0   0.2  32.0  18.0  31.0   0.2  31.0   0.7
 [4189]   4.0  18.0  18.0  92.0   4.0  30.0   0.2   4.0   4.0   4.0   4.0   4.0
 [4201]   4.0   0.2   4.0   0.2   0.0   4.0   4.0  18.0   0.2  13.0   4.0   4.0
 [4213]   4.0  31.0  31.0   4.0   5.0   5.0   0.2  31.0  31.0   4.0   4.0   4.0
 [4225]  39.0   4.0   4.0  18.0   4.0  31.0  31.0  31.0  32.0  32.0   0.2   4.0
 [4237]   0.2   0.2   4.0  30.0   1.0   0.2   4.0   4.0  31.0  39.0   1.0   0.2
 [4249]   4.0  18.0  30.0  31.0   3.0   4.0   4.0   4.0  18.0  18.0   4.0  18.0
 [4261]   4.0   4.0   4.0   0.2   4.0   0.5   1.0   1.0   3.0  42.0   0.2   4.0
 [4273]   4.0  18.0   0.2   3.0   1.0   2.0  31.0  30.0   0.2   4.0  30.0  26.0
 [4285]  18.0  31.0  30.0  18.0   4.0   4.0   4.0  31.0   0.2   4.0   4.0   0.4
 [4297]   4.0  32.0  32.0  33.0  31.0   4.0  31.0   4.0   4.0   0.2   4.0   4.0
 [4309]   4.0   4.0   3.0   4.0  31.0  31.0   4.0   4.0  39.0   0.2   5.0   4.0
 [4321]   4.0   4.0   4.0   4.0  31.0   4.0   4.0   5.0  31.0  31.0   2.0   4.0
 [4333]   4.0   4.0   0.2   4.0   4.0   4.0   4.0   4.0   5.0  31.0  31.0   4.0
 [4345]  18.0   4.0   4.0   4.0   4.0   4.0  18.0   4.0   0.2   4.0   4.0   4.0
 [4357]   4.0  27.0   4.0  31.0   4.0   4.0   0.2   1.0   4.0   1.0   4.0   0.2
 [4369]   4.0   0.3   3.0   4.0   0.2   5.0  15.0   4.0   4.0   0.2   3.0  18.0
 [4381]   4.0  31.0  18.0  18.0  18.0   4.0   4.0  31.0   1.0   4.0   4.0   4.0
 [4393]  39.0   4.0   4.0   0.2   6.0  18.0  31.0  43.0   4.0   4.0   4.0  31.0
 [4405]   2.0   4.0   4.0   4.0   4.0   4.0   4.0   2.0   4.0   4.0  31.0   1.0
 [4417]   4.0   3.0  18.0   1.0  31.0   0.2   4.0   2.0   2.0   4.0   6.0   5.0
 [4429]  31.0  18.0  43.0   4.0  19.0  31.0   4.0   4.0   3.0  31.0  31.0   0.2
 [4441]  31.0   2.0   4.0  31.0   4.0   1.0   4.0  18.0  27.0   0.2  31.0  25.0
 [4453]   1.0  31.0   1.0   4.0   1.0   4.0  18.0  30.0  23.0  32.0   4.0  31.0
 [4465]   0.2   0.2   0.1   1.0  31.0   0.2   2.0  18.0   4.0  19.0   4.0   2.0
 [4477]   4.0   4.0  31.0  18.0   4.0  31.0   0.2   0.1   4.0   0.2   4.0   4.0
 [4489]   4.0   4.0   4.0   4.0  31.0   4.0   0.2   1.0  18.0  23.0  31.0   0.2
 [4501]  18.0  31.0  31.0  31.0  31.0   4.0   4.0  18.0   4.0   4.0   4.0   4.0
 [4513]   4.0   4.0   4.0   0.2   3.0   4.0  18.0   4.0   4.0   2.0  31.0  31.0
 [4525]   0.2  18.0   4.0  18.0   4.0   4.0   4.0   0.2   6.0   4.0   0.2  26.0
 [4537]   1.0   0.2  18.0   4.0  19.0   4.0   4.0   4.0   4.0   0.2   0.8   4.0
 [4549]   4.0   4.0   4.0   4.0   2.0   4.0  32.0   1.0   4.0   0.2  18.0   4.0
 [4561]   0.2   4.0   4.0   4.0   4.0   6.0  32.0   4.0  31.0  31.0  18.0   4.0
 [4573]   4.0   1.0  18.0   4.0   4.0   4.0   4.0   0.6  32.0  19.0   0.2  32.0
 [4585]  19.0   4.0   4.0   0.2   4.0   4.0  31.0   4.0   4.0   4.0   4.0  32.0
 [4597]   1.0  18.0   4.0   4.0   4.0   0.2   4.0   4.0   4.0   0.2   4.0   0.2
 [4609]  19.0   4.0   4.0   1.0   4.0   4.0   4.0   4.0   4.0   4.0  18.0  27.0
 [4621]  31.0   4.0  32.0   4.0   0.2   4.0   4.0   2.0  19.0   4.0   1.0   7.0
 [4633]   4.0  32.0   4.0   4.0  32.0  31.0   1.0  31.0   0.2  19.0   0.6   4.0
 [4645]   0.2   4.0   0.2  19.0   6.0   4.0   0.2  31.0   4.0   4.0  18.0   0.2
 [4657]   4.0  31.0   4.0  44.0   4.0   4.0   4.0   4.0  18.0  19.0  33.0  32.0
 [4669]   4.0  31.0   4.0  32.0   0.2  23.0   0.2   4.0   4.0   4.0   7.0  27.0
 [4681]   1.0  31.0  31.0   0.2  18.0   4.0   4.0   1.0   4.0   4.0   4.0   4.0
 [4693]   4.0   4.0   4.0   4.0  19.0   4.0   4.0  32.0   4.0  31.0   4.0   4.0
 [4705]   3.0   0.2   1.0   4.0  32.0   4.0   4.0   0.2   0.2   4.0   0.2  32.0
 [4717]  91.0   4.0   4.0  32.0   1.0   4.0  18.0   4.0   4.0   4.0  32.0  18.0
 [4729]   2.0  32.0   4.0   2.0   4.0  40.0   0.2   0.2   4.0   4.0   1.0   0.9
 [4741]   0.2   4.0   2.0   4.0  31.0   1.0   0.2  45.0   4.0  31.0  29.0   4.0
 [4753]   4.0   4.0   4.0   4.0  18.0   4.0   4.0   4.0   0.2   4.0   4.0   4.0
 [4765]   4.0   6.0   0.0   4.0   4.0   4.0   0.2   4.0   4.0   0.2   0.2   4.0
 [4777]   4.0  32.0   4.0   4.0  31.0   4.0   4.0   4.0   4.0   2.0   0.2   2.0
 [4789]   4.0   7.0   4.0   4.0   4.0   4.0   4.0   2.0  31.0   4.0   1.0   4.0
 [4801]  29.0   4.0   0.2   4.0  31.0   2.0   4.0   4.0   6.0   4.0  29.0  32.0
 [4813]   2.0   0.2  32.0   4.0   4.0   0.7  32.0  32.0   4.0  18.0  31.0   4.0
 [4825]   4.0   4.0   2.0   0.2   0.1   4.0   5.0  18.0   0.2  31.0   5.0   4.0
 [4837]  40.0  26.0   5.0   4.0   5.0   4.0   5.0   5.0  19.0   5.0  21.0  32.0
 [4849]  32.0  19.0   0.2   5.0   5.0   5.0   5.0   0.2  46.0   5.0   5.0   5.0
 [4861]   5.0   2.0   7.0  18.0  32.0  33.0  32.0   5.0  13.0   5.0   5.0  18.0
 [4873]   5.0   5.0   5.0  33.0   5.0   5.0   0.0   0.2   0.7   5.0   1.0   5.0
 [4885]  31.0  14.0   5.0  31.0   5.0   2.0   0.2   5.0   1.0   1.0   5.0   5.0
 [4897]   5.0   5.0  31.0   2.0   5.0   0.2   0.2   5.0   0.2   5.0  33.0  18.0
 [4909]   5.0   0.3  31.0   0.2   5.0   5.0  32.0   5.0   0.3   5.0   3.0   0.2
 [4921]   0.3   5.0   0.2   5.0  32.0   5.0   2.0  18.0   5.0   5.0  32.0  46.0
 [4933]   5.0   0.3   5.0  20.0  28.0  27.0   5.0   0.3  10.0   5.0   5.0   5.0
 [4945]  19.0   5.0   5.0   0.4   0.2  32.0  32.0   5.0   0.3   5.0   5.0  32.0
 [4957]   5.0  32.0   0.2   3.0   5.0   0.6   5.0  19.0  32.0   0.3   5.0   5.0
 [4969]   4.0   5.0   5.0   5.0   1.0   0.2  41.0   5.0  18.0  19.0  46.0  18.0
 [4981]   5.0   5.0   5.0   0.3   5.0   5.0  32.0   5.0   5.0   5.0   5.0  18.0
 [4993]   5.0   5.0   2.0  29.0  32.0  32.0   5.0   5.0   5.0   5.0   5.0   0.2
 [5005]  33.0  39.0  34.0  32.0  18.0   5.0   5.0   4.0   0.3   5.0   5.0   5.0
 [5017]   5.0  41.0   5.0   2.0   0.5   7.0   0.2  19.0  31.0  32.0  15.0  41.0
 [5029]   5.0   5.0  32.0   5.0  29.0   5.0   2.0   5.0  18.0  32.0   3.0  33.0
 [5041]  32.0   5.0   5.0  19.0   5.0   5.0   2.0   5.0  14.0  19.0  32.0  19.0
 [5053]   5.0   1.0   2.0   5.0  47.0   5.0   5.0  12.0  32.0  32.0  11.0  32.0
 [5065]   0.2   5.0   1.0   8.0   5.0   5.0   0.6  41.0   0.3   2.0   2.0  19.0
 [5077]   5.0   5.0  48.0  19.0   5.0   5.0   5.0   0.4   0.2  32.0   5.0   0.2
 [5089]  32.0   0.2  48.0   0.2  32.0   5.0   5.0   5.0  19.0   0.5   0.2   0.3
 [5101]   5.0  32.0  18.0   1.0   4.0   5.0   0.3   0.3   5.0   5.0   5.0   5.0
 [5113]   2.0  32.0   5.0   5.0   5.0   0.3  32.0   0.3   5.0   5.0   5.0   5.0
 [5125]   5.0   5.0   5.0   5.0  18.0  48.0   0.3   0.3   5.0   5.0   5.0  48.0
 [5137]  38.0   0.4   8.0   1.0  32.0   0.3  32.0   5.0   5.0  18.0   5.0   5.0
 [5149]  41.0  32.0   5.0  26.0  32.0   1.0   5.0   0.3  32.0   0.3   5.0   5.0
 [5161]   5.0   3.0   9.0  14.0  18.0   5.0   5.0   5.0   5.0   1.0   1.0   5.0
 [5173]  43.0  15.0   0.3   6.0  33.0  26.0   5.0   0.3  32.0   5.0   5.0   9.0
 [5185]   5.0   5.0   1.0   0.0   5.0   1.0   5.0   1.0   5.0   5.0  43.0   0.5
 [5197]  32.0  32.0   0.3   0.3   0.3   1.0   0.8  32.0  32.0   5.0   5.0   5.0
 [5209]   5.0   5.0   5.0   0.3  32.0   5.0  32.0   0.3  32.0   1.0   5.0   5.0
 [5221]   5.0   0.3  18.0   2.0   3.0   5.0   5.0   2.0   5.0  32.0  32.0   5.0
 [5233]   5.0  32.0  32.0   5.0   5.0   5.0   5.0   5.0   5.0  32.0  44.0   5.0
 [5245]   3.0  32.0   6.0   1.0   5.0   0.3   5.0   4.0   5.0  32.0  18.0   0.3
 [5257]  45.0   5.0   5.0   5.0   1.0   5.0   1.0  18.0   5.0   6.0  18.0   5.0
 [5269]   5.0  32.0   5.0  19.0   0.3   7.0   0.3   4.0  33.0   1.0   4.0   5.0
 [5281]  16.0  18.0   0.3  18.0   5.0   5.0   2.0  49.0   5.0  34.0  34.0  35.0
 [5293]  35.0  43.0   5.0  47.0   5.0   0.7   9.0   5.0   9.0   1.0   0.3  32.0
 [5305]  32.0  32.0   5.0   0.3   1.0   5.0   1.0   5.0   0.3   5.0  47.0 119.0
 [5317]   5.0   9.0   5.0   5.0   5.0   5.0   5.0  18.0   5.0   5.0  32.0   0.2
 [5329]   5.0   0.3   5.0   5.0  12.0   5.0  33.0   5.0  33.0   0.3   5.0  32.0
 [5341]   5.0  16.0   5.0   5.0   1.0  18.0  19.0  19.0  29.0   5.0   5.0  32.0
 [5353]   5.0  16.0   5.0   5.0  10.0   0.3   5.0   5.0  32.0  33.0   5.0  47.0
 [5365]  51.0   5.0   0.3   0.3   5.0  48.0   1.0   5.0   0.3   5.0   0.3   5.0
 [5377]   3.0   0.3   2.0   1.0   5.0  35.0  35.0  36.0  33.0  32.0  33.0   5.0
 [5389]   5.0   5.0  51.0   1.0   5.0   2.0   3.0   5.0   5.0   3.0   5.0   5.0
 [5401]   5.0  51.0   2.0   5.0   5.0   5.0  32.0   1.0  33.0   5.0   4.0   5.0
 [5413]   2.0  33.0   5.0   9.0  18.0  29.0   1.0   2.0   5.0  18.0   5.0   5.0
 [5425]   0.3   5.0   5.0   5.0   5.0   5.0   5.0   5.0  33.0   5.0   5.0   4.0
 [5437]   5.0   0.3   5.0  14.0   5.0   5.0  33.0   2.0   5.0   2.0  33.0   5.0
 [5449]   5.0  33.0   0.0  33.0   0.6   5.0  18.0   5.0   5.0   5.0   5.0   2.0
 [5461]   5.0   5.0   5.0   5.0  33.0   7.0   5.0  33.0   5.0  19.0  51.0   5.0
 [5473]  18.0  19.0   0.3   5.0  18.0   5.0  33.0  19.0   5.0   5.0   5.0  18.0
 [5485]  49.0   1.0   0.3   5.0   5.0   9.0  29.0   0.3  33.0   5.0   5.0   0.3
 [5497]   0.3   5.0   0.3   9.0   5.0  33.0   0.3   5.0   3.0  19.0  33.0   0.3
 [5509]   5.0   0.3   0.0   5.0   0.3   3.0   5.0   0.3   0.5   0.4   5.0   5.0
 [5521]   9.0   5.0  10.0  24.0  36.0  37.0   4.0  33.0   5.0   5.0  15.0   5.0
 [5533]   2.0   0.3   3.0   4.0   5.0  33.0   5.0   5.0  33.0   5.0  33.0  19.0
 [5545]   5.0   5.0   5.0   5.0   1.0   5.0   5.0   5.0  14.0   4.0  33.0   5.0
 [5557]   5.0   5.0  19.0   1.0   0.3   5.0   5.0  15.0  33.0   5.0   5.0   5.0
 [5569]   0.3   5.0   5.0  49.0   0.0  16.0   5.0   0.0  33.0   5.0   5.0   5.0
 [5581]  19.0  50.0   5.0  33.0   2.0  33.0  33.0   5.0  19.0   4.0   5.0   5.0
 [5593]   5.0   7.0   1.0   5.0   5.0  33.0   5.0   5.0   0.3   5.0   0.3   5.0
 [5605]   1.0   2.0   5.0  18.0  33.0  33.0  30.0  33.0   0.3   5.0   5.0   5.0
 [5617]   0.2  41.0   5.0  19.0  18.0   4.0  10.0  33.0   5.0  19.0   1.0   5.0
 [5629]  33.0   0.9   5.0  33.0   5.0  10.0   5.0   5.0   4.0   5.0   5.0  51.0
 [5641]   5.0   5.0   5.0   0.5   5.0   4.0  33.0   5.0   5.0  19.0   5.0   5.0
 [5653]   5.0  33.0   5.0  33.0  33.0   2.0   5.0   5.0   5.0   5.0   0.2   5.0
 [5665]   5.0  33.0  33.0  51.0   5.0   5.0  19.0   4.0   5.0  33.0   0.3   5.0
 [5677]   5.0  19.0   0.3   7.0  37.0  37.0   5.0  28.0  19.0   5.0   5.0   0.1
 [5689]   0.3   1.0   5.0   5.0   5.0   5.0   5.0   5.0   5.0   5.0  51.0   0.3
 [5701]  52.0   2.0  14.0  10.0   5.0   5.0   5.0   5.0   0.6   5.0   7.0   5.0
 [5713]   0.3   2.0   4.0   5.0   0.6  15.0   5.0   5.0  51.0   5.0   5.0   0.3
 [5725]  33.0  19.0  33.0   5.0   5.0   5.0  33.0   5.0   5.0   5.0   5.0   5.0
 [5737]   2.0  19.0  19.0   5.0   0.6  33.0   0.3   5.0   3.0   5.0   0.1  18.0
 [5749]   0.3   0.0   5.0   0.3   4.0   5.0  51.0  33.0   0.3   3.0   5.0   5.0
 [5761]   0.3  33.0  33.0   5.0   0.3   5.0   5.0  19.0   4.0   0.6   5.0  19.0
 [5773]  19.0   0.1   0.3   5.0   7.0  14.0  29.0  33.0   5.0  33.0   0.3   6.0
 [5785]   2.0  33.0   6.0   0.3   6.0   6.0   5.0   5.0   6.0   1.0  33.0   2.0
 [5797]   1.0   5.0   0.3  10.0   0.7   6.0   1.0   6.0   5.0  52.0  51.0   6.0
 [5809]   6.0  33.0  18.0   6.0   6.0   6.0   0.3   6.0   6.0   6.0   0.0   6.0
 [5821]  14.0   8.0   2.0   6.0   6.0   6.0   6.0  33.0   0.3   6.0   6.0   6.0
 [5833]   6.0   6.0   6.0   6.0  30.0  18.0  33.0  33.0   6.0   0.3  15.0   6.0
 [5845]   0.2   6.0   6.0   2.0   0.3  19.0   6.0   6.0   6.0   6.0   6.0   6.0
 [5857]   6.0   6.0   6.0   6.0  52.0   0.3   6.0   6.0  33.0   6.0   0.3  33.0
 [5869]   6.0   0.4  33.0  18.0   0.3   0.3   0.3  19.0  92.0   6.0   4.0   0.6
 [5881]  19.0  18.0   6.0   1.0   6.0   6.0   0.3   6.0   6.0   0.5   6.0   6.0
 [5893]   0.2   0.3   2.0  33.0   0.0   0.3   0.4   6.0   6.0  33.0  53.0   6.0
 [5905]   0.3   6.0   6.0   6.0   6.0  18.0  33.0   0.2   4.0   6.0   0.3   0.4
 [5917]   6.0   6.0   6.0  19.0   6.0   6.0   6.0   0.2  33.0   6.0   2.0   6.0
 [5929]   4.0   6.0   6.0   0.4  19.0   6.0   3.0  19.0   6.0   6.0   2.0   0.5
 [5941]  33.0   6.0   2.0   6.0   6.0   6.0   6.0   0.3   2.0  33.0  33.0  33.0
 [5953]  33.0  33.0   5.0  52.0  52.0  19.0   6.0   6.0   6.0  52.0   0.3  30.0
 [5965]   0.2   6.0   6.0   1.0  33.0  19.0   6.0   1.0  19.0   6.0   6.0   6.0
 [5977]   1.0   6.0  53.0  33.0   6.0  19.0   2.0   6.0   6.0   6.0   6.0   4.0
 [5989]   0.8  27.0  33.0  53.0   6.0   0.2   4.0   0.5  19.0   6.0   0.3   0.3
 [6001]  25.0   6.0  34.0   0.2  92.0   6.0  30.0   6.0   6.0   6.0  33.0  30.0
 [6013]   6.0   0.3   7.0   8.0  10.0  27.0  34.0  34.0   6.0   0.3   6.0   6.0
 [6025]   4.0   6.0  19.0   0.1  19.0  19.0   4.0  19.0   6.0   6.0  34.0  10.0
 [6037]   0.1   6.0  34.0   6.0   2.0   0.8   6.0   4.0   6.0  18.0  13.0   6.0
 [6049]   2.0   3.0  19.0   6.0   6.0  93.0   0.3   6.0  34.0  34.0  34.0  19.0
 [6061]   6.0   2.0  34.0   1.0   6.0   6.0  19.0   6.0  34.0   6.0   6.0   0.3
 [6073]   6.0  34.0  53.0   6.0  34.0   6.0   1.0   2.0   2.0   6.0   1.0   6.0
 [6085]   6.0   6.0   6.0  19.0   6.0   6.0  34.0   6.0   6.0   6.0   6.0  34.0
 [6097]   6.0   4.0   1.0   0.3   6.0   0.3   0.9  34.0  34.0   6.0  54.0  16.0
 [6109]  19.0   6.0   0.3   0.4   6.0  20.0   6.0   6.0  34.0   6.0   0.6   1.0
 [6121]   0.4   6.0   6.0   6.0   6.0   0.3   8.0  34.0   0.2   2.0   1.0  19.0
 [6133]  34.0   6.0   6.0   6.0   6.0   6.0   6.0   0.3  34.0   1.0   6.0   6.0
 [6145]   1.0   0.3   6.0  10.0   6.0   6.0   6.0   6.0  34.0   0.0   0.3   6.0
 [6157]   1.0   6.0   0.3  19.0  34.0   0.4   6.0  34.0   6.0   0.3   6.0   0.5
 [6169]   0.3   6.0   0.4   6.0   6.0   5.0   6.0   6.0   6.0   1.0   0.4   6.0
 [6181]   6.0   6.0  10.0   2.0   1.0  34.0   6.0  18.0  53.0   6.0   6.0   6.0
 [6193]  30.0  19.0   6.0  10.0   0.4  34.0   2.0   6.0   6.0  34.0   2.0  55.0
 [6205]  19.0   6.0  34.0   6.0   6.0   6.0   6.0   0.3  18.0   6.0   4.0  18.0
 [6217]  34.0   6.0  34.0   0.3  18.0   1.0  34.0   6.0   6.0   6.0   0.4   6.0
 [6229]  34.0   6.0   6.0   0.9   6.0   0.3   1.0   6.0   1.0  55.0   6.0   0.9
 [6241]   6.0   6.0   6.0  14.0  33.0  37.0  28.0   6.0   6.0   2.0  34.0   6.0
 [6253]  34.0   1.0   6.0  28.0   0.3   6.0   6.0   6.0   0.3   6.0  18.0   6.0
 [6265]  19.0   8.0  30.0  34.0   6.0  27.0  56.0  10.0  19.0  34.0  30.0   6.0
 [6277]   0.3   6.0   6.0   6.0   0.2  34.0   1.0   6.0   0.7   6.0   0.4   6.0
 [6289]   6.0   0.4   2.0   6.0  34.0   6.0   6.0  19.0  33.0  34.0  19.0  19.0
 [6301]   4.0  92.0   6.0   6.0  57.0   0.4  34.0   2.0   6.0  34.0  19.0  34.0
 [6313]  34.0   1.0   6.0   6.0   6.0  34.0   6.0   6.0  19.0   6.0   6.0   6.0
 [6325]   6.0   6.0   2.0  34.0   0.2   6.0   2.0   6.0  19.0   6.0   4.0   0.3
 [6337]   6.0   0.6   0.3   6.0   6.0   6.0   6.0  34.0   4.0   4.0   6.0   6.0
 [6349]  19.0  19.0   1.0  34.0   5.0   0.3   0.4   6.0   6.0   6.0   6.0   6.0
 [6361]   6.0  19.0   6.0  10.0  10.0  34.0   6.0   0.1  32.0   6.0   6.0  19.0
 [6373]   6.0   2.0  34.0  30.0   2.0  34.0  34.0   6.0   6.0  34.0   6.0   0.4
 [6385]   0.1   6.0   1.0  34.0  18.0   0.4   6.0   1.0   6.0   6.0   0.3  19.0
 [6397]   6.0   6.0   6.0   0.4   0.4   3.0   6.0  19.0   6.0   6.0   6.0   4.0
 [6409]   0.4   0.4  34.0   2.0   6.0   0.4   0.4  34.0  54.0   1.0   0.4   6.0
 [6421]  19.0   6.0   1.0   8.0  34.0  34.0   4.0   6.0  34.0   0.4   6.0   5.0
 [6433]   6.0   0.4   1.0  19.0   6.0   6.0   6.0   6.0   6.0   8.0  33.0  34.0
 [6445]   1.0   0.4   6.0   6.0  19.0   6.0   6.0   0.4   0.4   0.4  34.0  34.0
 [6457]   6.0   0.5  35.0   3.0   6.0   6.0   1.0   4.0   6.0  35.0   0.4   6.0
 [6469]   0.3   6.0   6.0  19.0   0.4  57.0   0.8   6.0   6.0   6.0   1.0  46.0
 [6481]   6.0   6.0  19.0  34.0  18.0   5.0   0.4  57.0   6.0   6.0   6.0   6.0
 [6493]   2.0  16.0   8.0   6.0  19.0   0.8   0.4   5.0   6.0  58.0   6.0   6.0
 [6505]   6.0  35.0   6.0  35.0   6.0   6.0   6.0   6.0   0.3  54.0   0.4   0.4
 [6517]  35.0  19.0   6.0   0.4   0.4  35.0   6.0   6.0   6.0   0.1   6.0   0.4
 [6529]   6.0  15.0  28.0   7.0   5.0   6.0  34.0  19.0   0.4   0.4   0.4   6.0
 [6541]   2.0   2.0   6.0   6.0   0.1   1.0  35.0  19.0   6.0   0.4   6.0   6.0
 [6553]  58.0  31.0   6.0   6.0   6.0  18.0   2.0   6.0  35.0   6.0   1.0   6.0
 [6565]   6.0   6.0   0.4  35.0   3.0  35.0   0.4   6.0  35.0  93.0   6.0   6.0
 [6577]   0.4   6.0   6.0   6.0  18.0   6.0  32.0  19.0   0.4   6.0   6.0  19.0
 [6589]  34.0  38.0  38.0  34.0   6.0   6.0  19.0   1.0   0.4   6.0  54.0  18.0
 [6601]   6.0  23.0  19.0   6.0   6.0   6.0   6.0  31.0  18.0  34.0  34.0  18.0
 [6613]  35.0   6.0  19.0   6.0   2.0  34.0   0.4   6.0   1.0   6.0   1.0   6.0
 [6625]   6.0  35.0  35.0   4.0   6.0   0.2   6.0   0.4   6.0   0.4   6.0   2.0
 [6637]   6.0   2.0   6.0   2.0   6.0   0.4   0.3   0.4   6.0  55.0  19.0   6.0
 [6649]  34.0   5.0   6.0   6.0   0.4   0.4   6.0  19.0  34.0  10.0  35.0   6.0
 [6661]   6.0   6.0   2.0  34.0   0.4  34.0   6.0   2.0   6.0   6.0   2.0   6.0
 [6673]  35.0  34.0   0.2   3.0  18.0   6.0   6.0   5.0   0.4   6.0  35.0   6.0
 [6685]   0.4   6.0   6.0  34.0   6.0   6.0  35.0  58.0  35.0   6.0   0.7   6.0
 [6697]   6.0   4.0   4.0   6.0   0.7   6.0  35.0   4.0  19.0   6.0  31.0   4.0
 [6709]   6.0   6.0   5.0  17.0   2.0   6.0   0.8   2.0   5.0   2.0   6.0   1.0
 [6721]  91.0   6.0   6.0   6.0   4.0  59.0   6.0   7.0   2.0  93.0  59.0  18.0
 [6733]   0.0   6.0   6.0   7.0  35.0  35.0  35.0   7.0  35.0  55.0   0.8   6.0
 [6745]   6.0   0.4   7.0   6.0   6.0   6.0  34.0  38.0  35.0   7.0   7.0   2.0
 [6757]   7.0  19.0   7.0   7.0   0.4   7.0  35.0  35.0  35.0  35.0  35.0   7.0
 [6769]   7.0   7.0   5.0   7.0  35.0   6.0   0.4  31.0   3.0   8.0   7.0   0.0
 [6781]   6.0   0.4  19.0  35.0   0.4   7.0   7.0   0.4   7.0  18.0   7.0   7.0
 [6793]   7.0   7.0   7.0   7.0   7.0   7.0   7.0   7.0   7.0   7.0   7.0   2.0
 [6805]   7.0   0.8   7.0   2.0  10.0   0.4  35.0   7.0   0.2   0.4   7.0   7.0
 [6817]   7.0   0.7  55.0   7.0  49.0   0.4   7.0   2.0   7.0   7.0   2.0   6.0
 [6829]  33.0  35.0   0.4   1.0   7.0   7.0   7.0   7.0   7.0   7.0  30.0   7.0
 [6841]   7.0   4.0   0.1  35.0   7.0   0.1   7.0   7.0   7.0   4.0   0.4  35.0
 [6853]   7.0   7.0  35.0   0.4   0.4   7.0   7.0  59.0   0.0  35.0   0.4   7.0
 [6865]   7.0   0.4   0.4  19.0   4.0   7.0   7.0   7.0   7.0   7.0  35.0  35.0
 [6877]   7.0   7.0   6.0   7.0   0.4   7.0   7.0   1.0   7.0   7.0   7.0  59.0
 [6889]  34.0   2.0  31.0  35.0  18.0   7.0   0.7   4.0   7.0   0.4   7.0   7.0
 [6901]   7.0   7.0   7.0   0.4   7.0   7.0   7.0  19.0   5.0  19.0   7.0   8.0
 [6913]  34.0  35.0   0.4   0.4  35.0   2.0  31.0  19.0  19.0   7.0   7.0  19.0
 [6925]   0.1   0.5  35.0   0.4   0.4   7.0   1.0   7.0   7.0  35.0  19.0   7.0
 [6937]   7.0   0.5   7.0   0.9   9.0  19.0   0.9   7.0   0.9   7.0  18.0  32.0
 [6949]   0.4   0.8  19.0   3.0   7.0   7.0   7.0   7.0   7.0   7.0  35.0  19.0
 [6961]   7.0   7.0   7.0   7.0   7.0   2.0   7.0   0.4   7.0  35.0  35.0  35.0
 [6973]   0.4   7.0   7.0   7.0   7.0  19.0   7.0   6.0   1.0   0.4  10.0  11.0
 [6985]  19.0  35.0   1.0  19.0   1.0   7.0   7.0   0.4   7.0   7.0   7.0   7.0
 [6997]   0.4   7.0  19.0   7.0   7.0   7.0  19.0   7.0  19.0   1.0   7.0   7.0
 [7009]   7.0   7.0   1.0   0.8   0.3   0.4   7.0   4.0   7.0   7.0   0.2   7.0
 [7021]  11.0  35.0  19.0   7.0  19.0   7.0   7.0   7.0  19.0   9.0   2.0  32.0
 [7033]   7.0   7.0   7.0  56.0   7.0   0.4   7.0   7.0   1.0   7.0   7.0   7.0
 [7045]   0.4   7.0  19.0  11.0  35.0   7.0   0.4   1.0   7.0   7.0  19.0   7.0
 [7057]   1.0   0.9   7.0   7.0  10.0  36.0   7.0  35.0  56.0   7.0   7.0   7.0
 [7069]   7.0  19.0   0.4   5.0   7.0   1.0   9.0  34.0  38.0   7.0   4.0   0.4
 [7081]   7.0  17.0   0.4  11.0   9.0   7.0  35.0  12.0   1.0   7.0  94.0   0.4
 [7093]  18.0   0.4   2.0   5.0   0.4  18.0   3.0   7.0   7.0  19.0   0.4   7.0
 [7105]   1.0   7.0   7.0   7.0   7.0   2.0  19.0   7.0  19.0   8.0  35.0  36.0
 [7117]  19.0   0.6   0.4   7.0  17.0   0.1   0.4   7.0  36.0   7.0   1.0   0.1
 [7129]   7.0   0.4   7.0   6.0   2.0  36.0  35.0  35.0  31.0   7.0   7.0   7.0
 [7141]   7.0   7.0  35.0   0.4   7.0   0.4   7.0   7.0   7.0   7.0   0.5   7.0
 [7153]   7.0   7.0   7.0  36.0   7.0   0.4   7.0   9.0   9.0   7.0   7.0  59.0
 [7165]  35.0   7.0   0.0  36.0   7.0   7.0   7.0   0.4   7.0  19.0  18.0  34.0
 [7177]  91.0   0.4  35.0  19.0   7.0   0.4   7.0   6.0   7.0   7.0   0.5   4.0
 [7189]   1.0   7.0   7.0   0.4   7.0   0.8  16.0  11.0   7.0  35.0  36.0   7.0
 [7201]   7.0   0.8   6.0  39.0  39.0  35.0  19.0  19.0  36.0   7.0   7.0   7.0
 [7213]   4.0   1.0   0.4   7.0   4.0   0.5   0.4  36.0   7.0   7.0   1.0   7.0
 [7225]   7.0  35.0  17.0  35.0  28.0   0.4   7.0   7.0   0.0   7.0   7.0  17.0
 [7237]  36.0   5.0   7.0   7.0  35.0  18.0   0.1   6.0   7.0   1.0  34.0  32.0
 [7249]  18.0   7.0   7.0  20.0   7.0  35.0   0.4   7.0   7.0   0.4   7.0   7.0
 [7261]  36.0   7.0   7.0   7.0  35.0   7.0  36.0   7.0  93.0   1.0   7.0   3.0
 [7273]  36.0  19.0   7.0  18.0  27.0   6.0   7.0   7.0  31.0  32.0  34.0  19.0
 [7285]   9.0   7.0   7.0   7.0   7.0   2.0   7.0   7.0   0.4   0.4   0.9   6.0
 [7297]  30.0   0.4   7.0   7.0   0.5   7.0  19.0   5.0  19.0  36.0   7.0   7.0
 [7309]   7.0  57.0  11.0   0.5   7.0  18.0   7.0  35.0   7.0   7.0   5.0   7.0
 [7321]   7.0   7.0  35.0  36.0   7.0  35.0   1.0   0.5   0.4   0.4   0.0   7.0
 [7333]   7.0   7.0  35.0  59.0   7.0  35.0  11.0   7.0   7.0  32.0  11.0  64.0
 [7345]   7.0  36.0   7.0   0.5  36.0   7.0   7.0   7.0   7.0  32.0   7.0   7.0
 [7357]   7.0  35.0  18.0   0.4   7.0   7.0   7.0   0.4  36.0  35.0   2.0  36.0
 [7369]   7.0  19.0  19.0  57.0   5.0   7.0   0.4   0.1   7.0  59.0   0.5   7.0
 [7381]  36.0  35.0   4.0   7.0   0.5  19.0   0.4   0.5   0.0  35.0  35.0  19.0
 [7393]   7.0   3.0  36.0   7.0  19.0   7.0   0.5  36.0  35.0   6.0   7.0   7.0
 [7405]   6.0   9.0  11.0   8.0   7.0   8.0   7.0   8.0   0.4  58.0   7.0   8.0
 [7417]  35.0  36.0  58.0   0.9   8.0   7.0   8.0   7.0  18.0   8.0  36.0  31.0
 [7429]   7.0  36.0   5.0   8.0   7.0   7.0   8.0  35.0   7.0  29.0  36.0  35.0
 [7441]   7.0   8.0   8.0  18.0  36.0   0.5   7.0   7.0   7.0   0.5  36.0   7.0
 [7453]  60.0   8.0   4.0   0.6   0.5   7.0   9.0   8.0   0.3  36.0  16.0   8.0
 [7465]  19.0   0.5   8.0   7.0   8.0   7.0  14.0   8.0  36.0  36.0   7.0   1.0
 [7477]   8.0  59.0  59.0   8.0  36.0   8.0  36.0  59.0   8.0   0.3   7.0   7.0
 [7489]   0.5  33.0  19.0   7.0   8.0   0.6   7.0  36.0   0.5  19.0   0.5  24.0
 [7501]   7.0   8.0   9.0   0.5   7.0  18.0   7.0   8.0   7.0  19.0   8.0   8.0
 [7513]   7.0   8.0   1.0   7.0   7.0   8.0   4.0   7.0   7.0   7.0   7.0  37.0
 [7525]   0.2   2.0   7.0   8.0   8.0  19.0   8.0  36.0   1.0   8.0   8.0   8.0
 [7537]   8.0   8.0  37.0  36.0  36.0  19.0   2.0   0.5  19.0   8.0   8.0   8.0
 [7549]   8.0   2.0  37.0   8.0   8.0  19.0   0.5   8.0   3.0   2.0   0.0  17.0
 [7561]  11.0   8.0   8.0   0.5  36.0   1.0   8.0   8.0   0.8   8.0   0.5   0.5
 [7573]   8.0   8.0   1.0   2.0   8.0   1.0   8.0   8.0  25.0   0.5   8.0   8.0
 [7585]   0.5   0.5   0.5   8.0   8.0  19.0   8.0   8.0   8.0  37.0   0.5  19.0
 [7597]   0.5   8.0  19.0   8.0   8.0   8.0  10.0  33.0  36.0  36.0  36.0  37.0
 [7609]  59.0   0.5   1.0  29.0  36.0   1.0   0.5   8.0  37.0   0.5  60.0  19.0
 [7621]   8.0  11.0   0.5   2.0   5.0   9.0   0.5   8.0   8.0   2.0  19.0   8.0
 [7633]   8.0  19.0  36.0  36.0  37.0   2.0   0.5   6.0   1.0  36.0   0.0   8.0
 [7645]   8.0  36.0   8.0   8.0   8.0   8.0  27.0  13.0   8.0   1.0   8.0  60.0
 [7657]  59.0   0.6  26.0   3.0  37.0  37.0  60.0   6.0   8.0   8.0   8.0   8.0
 [7669]  37.0   1.0   5.0   8.0  19.0   8.0   0.5  18.0   8.0  37.0   4.0  36.0
 [7681]   7.0   0.5   0.5  36.0   8.0  36.0   8.0  36.0   8.0  36.0  16.0   8.0
 [7693]  30.0   8.0   8.0   1.0   8.0   8.0  35.0  36.0   8.0   4.0   0.5  36.0
 [7705]   8.0   8.0   8.0   8.0   0.8   8.0   8.0   8.0   8.0  33.0   8.0  19.0
 [7717]   0.8   8.0   8.0  37.0   8.0   8.0   0.5   8.0   8.0   5.0   8.0   8.0
 [7729]   8.0   8.0  37.0   1.0  30.0   1.0  18.0   0.7   8.0   0.5   8.0   8.0
 [7741]   8.0  19.0  36.0  37.0  36.0   8.0   8.0   8.0  19.0  36.0   8.0   2.0
 [7753]   0.5   8.0  19.0   7.0   8.0  19.0  40.0   8.0  19.0  11.0   0.5   8.0
 [7765]   8.0   8.0  37.0   0.5   8.0   8.0   8.0   0.5   8.0  19.0  19.0  19.0
 [7777]   0.5  39.0   8.0  19.0  36.0  19.0  19.0  36.0   7.0   8.0  19.0   8.0
 [7789]   8.0  19.0   6.0  60.0  60.0   8.0  39.0  36.0  40.0   8.0  24.0   8.0
 [7801]  37.0  36.0  19.0   8.0   8.0   0.7   8.0   8.0   7.0   4.0   0.5   8.0
 [7813]   2.0   3.0  37.0   8.0   1.0  19.0   8.0  19.0  34.0  11.0  37.0  37.0
 [7825]   1.0   8.0   8.0   0.5   0.5   8.0   8.0   7.0   8.0  36.0  33.0  36.0
 [7837]   8.0  19.0   8.0   8.0   8.0  14.0   0.5  37.0   2.0   8.0   8.0  37.0
 [7849]  37.0   7.0   8.0   8.0   7.0   8.0  37.0  37.0  36.0   0.3   0.0   8.0
 [7861]   8.0   8.0  36.0   2.0  37.0   0.5   0.5   8.0   8.0   0.5   8.0  37.0
 [7873]   0.5   8.0  20.0  20.0  32.0   8.0   8.0  20.0  19.0  19.0   0.5   8.0
 [7885]   8.0  37.0   0.2   8.0  38.0   8.0   8.0  38.0   8.0   1.0   8.0  11.0
 [7897]  38.0  38.0   8.0   1.0   0.5   1.0   4.0   8.0  18.0   8.0   8.0   8.0
 [7909]  20.0  16.0  36.0   8.0   8.0   8.0   1.0   3.0   8.0  20.0  19.0   8.0
 [7921]   8.0  94.0   8.0   8.0  11.0  19.0  12.0   8.0   8.0  36.0   8.0   8.0
 [7933]   0.5   8.0   8.0  34.0  61.0   8.0  38.0   1.0   8.0   4.0   8.0   8.0
 [7945]   3.0   8.0   8.0  19.0   8.0  60.0   8.0   0.3   2.0   8.0   8.0   8.0
 [7957]   8.0   0.5  60.0   8.0  38.0  37.0   1.0   8.0   8.0   8.0   8.0   8.0
 [7969]   8.0   1.0  38.0   8.0   8.0   8.0   8.0   8.0   8.0   8.0   8.0   8.0
 [7981]   8.0   0.5   0.5  19.0  38.0   8.0   8.0   0.6   8.0   8.0   8.0   0.5
 [7993]  62.0   1.0   2.0   8.0  20.0  37.0   8.0  15.0  19.0  37.0   3.0   0.5
 [8005]   0.5   8.0   8.0   8.0   8.0   8.0   8.0   4.0   8.0   8.0   8.0   0.3
 [8017]   8.0   8.0  36.0   8.0   8.0   8.0   8.0   8.0   0.5  19.0   8.0   8.0
 [8029]   8.0  15.0   8.0  37.0  27.0   0.2  20.0  38.0  37.0   7.0   8.0  18.0
 [8041]  27.0  37.0   8.0   8.0   8.0   2.0   8.0   8.0   1.0   0.0   1.0   0.5
 [8053]   8.0  38.0   0.0  31.0   8.0  19.0   6.0   0.2   8.0   8.0   0.5   8.0
 [8065]  30.0  19.0   8.0  18.0   8.0   8.0  18.0  12.0  37.0   8.0  18.0   8.0
 [8077]   8.0  37.0   8.0  19.0  37.0   8.0   8.0   8.0   8.0  19.0  37.0  34.0
 [8089]   9.0   8.0   8.0   8.0   8.0   0.7   8.0   9.0  38.0   0.5   2.0  36.0
 [8101]   1.0  38.0  37.0   0.5   0.5   0.8   8.0   0.5   9.0   1.0   9.0   0.8
 [8113]  33.0   8.0   3.0   9.0   9.0   0.5   9.0   8.0   0.5  20.0   9.0   0.1
 [8125]   9.0   3.0  20.0   8.0   6.0   1.0   8.0  60.0   9.0   8.0   8.0   6.0
 [8137]  38.0  30.0   0.1   8.0   8.0   8.0  38.0  19.0   8.0  14.0   0.4  37.0
 [8149]  18.0   9.0   0.1   0.5   1.0   9.0   9.0   0.5   9.0   9.0  10.0  15.0
 [8161]   9.0  37.0   9.0   9.0   9.0   0.8   9.0   0.5  10.0  42.0   9.0   9.0
 [8173]   2.0  38.0   9.0   9.0  38.0  44.0   9.0   9.0   3.0   0.5   9.0   0.5
 [8185]   9.0   8.0   9.0   9.0   9.0   9.0   9.0   4.0  11.0   1.0   9.0   8.0
 [8197]   9.0   9.0   9.0  37.0   9.0   9.0   2.0   1.0   9.0  62.0  20.0  20.0
 [8209]   9.0   0.5   9.0   1.0   9.0   0.5   3.0   2.0   0.5   2.0   0.5   9.0
 [8221]   7.0   0.5   0.5   0.1   0.5   9.0   9.0   0.6  38.0  38.0  37.0   9.0
 [8233]   0.5  20.0   9.0  19.0  36.0  40.0  38.0   2.0   9.0   9.0  35.0  38.0
 [8245]   0.5  37.0   9.0   5.0   1.0   4.0  38.0   9.0   0.5   2.0   4.0   9.0
 [8257]  34.0   9.0   0.5   9.0  20.0   9.0   0.5  34.0  37.0  37.0   9.0   0.8
 [8269]  60.0  24.0   9.0  10.0   9.0   9.0  37.0  38.0   9.0   9.0  35.0   9.0
 [8281]   4.0  38.0   9.0   9.0   5.0   9.0   0.1   9.0  38.0  37.0   9.0  37.0
 [8293]   7.0   9.0   9.0   9.0   8.0  37.0   9.0   9.0   2.0  38.0  39.0   0.2
 [8305]   3.0   9.0   9.0   9.0   9.0   9.0   9.0   9.0  12.0   1.0   3.0   9.0
 [8317]   9.0   2.0   9.0  20.0   9.0  10.0   9.0  39.0  27.0   9.0   1.0  19.0
 [8329]   9.0   9.0   0.5   9.0  18.0   9.0   0.5   9.0   9.0   9.0  39.0  37.0
 [8341]  19.0   9.0   0.8   0.5   9.0   9.0   9.0  61.0   9.0   1.0   3.0  25.0
 [8353]   0.5   9.0  19.0   9.0   0.5  19.0  37.0   9.0  37.0   0.5   9.0   4.0
 [8365]  20.0   9.0  37.0   9.0  12.0   0.6  39.0   1.0   9.0  39.0   0.8   9.0
 [8377]  61.0   9.0   0.4   9.0   3.0  63.0  20.0  37.0   9.0   0.5   0.5   9.0
 [8389]  38.0   0.5   9.0   9.0  38.0   2.0   9.0   9.0   9.0  39.0  38.0   0.5
 [8401]   2.0   9.0  62.0   1.0  39.0   9.0   0.5   0.4   9.0   9.0   1.0   1.0
 [8413]   9.0  20.0  25.0   9.0  10.0   2.0  39.0  18.0   9.0  20.0   2.0   4.0
 [8425]   9.0   0.5   9.0   9.0  20.0   9.0   2.0   9.0   9.0   9.0   0.5   2.0
 [8437]  20.0   9.0   9.0   9.0   1.0   0.5   0.5   9.0   9.0   9.0   9.0   0.5
 [8449]   9.0   0.5  19.0  38.0   9.0   9.0   9.0   3.0  39.0   7.0   6.0   1.0
 [8461]  38.0   9.0   9.0   9.0   9.0   2.0  20.0  38.0   9.0   9.0   9.0   9.0
 [8473]   9.0   9.0   9.0  35.0   9.0   6.0   0.5   9.0   9.0   9.0   9.0   9.0
 [8485]  20.0   9.0  10.0   9.0   9.0  38.0   1.0  64.0   9.0   9.0   9.0   3.0
 [8497]   9.0  11.0  35.0  40.0  36.0  36.0  37.0  38.0   0.5   4.0   9.0   1.0
 [8509]  19.0   9.0   9.0   9.0  10.0   5.0  15.0  39.0  62.0   9.0   1.0   7.0
 [8521]   9.0   9.0   9.0   9.0  15.0  38.0  32.0  39.0   9.0   9.0   9.0   0.5
 [8533]   9.0  19.0   9.0   9.0   9.0   1.0   9.0   6.0  62.0   5.0   0.5   0.5
 [8545]   3.0   0.5   9.0   1.0   0.5  38.0   9.0   9.0   0.5   0.8   9.0   9.0
 [8557]  19.0   9.0   9.0   9.0   9.0  38.0  39.0   9.0   2.0   8.0  64.0   9.0
 [8569]   9.0   6.0   9.0   9.0   9.0  38.0   6.0  20.0  38.0  39.0   9.0  38.0
 [8581]   2.0  20.0   9.0   1.0   9.0   0.5   4.0   9.0   9.0   9.0  38.0   9.0
 [8593]   9.0   0.5   5.0   9.0  20.0  39.0   9.0  39.0  19.0  18.0   9.0   9.0
 [8605]   9.0  64.0   9.0  62.0   0.5   9.0  15.0   9.0   9.0   0.1   9.0   9.0
 [8617]  19.0  38.0   9.0  19.0   1.0   9.0  63.0   0.5   9.0   4.0  20.0   9.0
 [8629]  10.0   9.0   9.0   9.0   9.0  38.0  38.0   0.5   2.0   9.0   9.0   7.0
 [8641]   0.5   9.0   9.0   9.0  39.0   9.0   0.1   9.0   0.5   2.0  64.0   0.5
 [8653]   0.2  65.0   9.0   9.0   9.0   4.0   9.0   9.0  38.0   4.0   9.0  39.0
 [8665]   9.0   9.0   9.0  10.0   0.5   0.5   9.0   1.0   9.0  13.0   9.0   0.5
 [8677]  19.0   0.5   9.0   9.0   9.0  10.0   9.0  11.0   9.0   9.0   9.0   9.0
 [8689]   9.0  11.0  27.0   0.4   9.0  38.0  38.0  38.0  39.0   1.0   0.2  62.0
 [8701]   2.0   9.0  63.0   9.0  63.0   0.5   9.0   4.0   0.5   1.0   1.0   9.0
 [8713]  39.0   9.0  39.0   9.0   9.0   9.0   9.0   0.5   5.0  11.0  11.0   2.0
 [8725]   9.0   9.0  39.0  39.0  63.0   9.0   3.0   9.0   9.0   9.0   9.0   9.0
 [8737]   8.0   9.0   9.0   2.0   1.0   9.0  20.0  39.0   9.0  63.0  35.0  12.0
 [8749]   0.5  18.0   1.0   9.0  39.0   0.5  19.0   9.0   9.0  39.0   0.6  39.0
 [8761]   0.5   0.5   6.0  20.0   9.0   1.0   9.0   9.0  19.0  39.0  40.0  19.0
 [8773]   3.0   9.0   1.0  40.0   9.0  19.0   9.0   2.0   9.0   9.0  40.0   9.0
 [8785]   9.0  40.0  19.0   9.0  20.0   9.0   9.0   9.0   9.0  20.0   2.0   9.0
 [8797]   3.0   9.0   8.0   6.0   9.0   1.0   0.5  13.0   0.5  40.0   0.5  20.0
 [8809]  40.0   2.0   2.0  31.0   0.0   9.0  40.0  10.0   0.6  10.0   4.0   9.0
 [8821]  10.0  40.0  10.0   9.0   7.0  20.0   9.0   9.0  13.0  18.0   3.0   9.0
 [8833]   9.0   9.0  10.0   9.0  10.0   0.7  10.0  18.0  10.0  40.0  20.0   4.0
 [8845]  10.0   5.0   9.0  65.0   9.0   0.5   9.0   9.0   0.5  10.0  12.0  13.0
 [8857]  12.0  10.0  40.0  10.0   0.5   0.5  10.0   9.0   9.0   3.0   9.0  10.0
 [8869]  39.0   9.0  40.0  24.0  15.0  24.0  19.0   9.0  10.0   9.0   9.0  66.0
 [8881]  10.0  20.0  19.0  37.0  10.0  10.0   1.0   2.0  10.0   2.0  10.0  10.0
 [8893]   1.0   5.0  10.0  20.0   1.0  35.0  13.0   0.5   2.0  10.0  39.0  10.0
 [8905]   1.0   0.2  10.0   0.6  40.0   6.0  20.0  39.0  10.0  10.0  10.0   6.0
 [8917]  20.0  10.0  10.0   0.2  18.0  39.0  10.0   0.6  10.0  10.0   0.6   6.0
 [8929]   2.0  10.0   6.0  10.0  10.0  10.0   3.0  10.0  10.0  40.0  18.0   4.0
 [8941]  39.0  10.0  10.0   1.0  10.0  10.0  39.0  10.0  10.0  10.0   1.0   1.0
 [8953]   9.0  10.0  96.0  10.0   0.5  18.0   0.5  10.0   0.6   6.0   0.2  10.0
 [8965]  39.0  10.0  39.0   3.0   2.0  10.0  12.0  19.0   7.0  10.0  10.0   0.3
 [8977]   0.6  10.0  10.0  10.0  39.0  20.0  40.0   0.6  10.0  10.0  10.0  39.0
 [8989]   8.0  10.0  10.0  10.0  10.0  10.0   4.0  10.0  10.0  10.0  18.0  44.0
 [9001]  10.0   0.6  10.0  10.0   3.0  10.0  10.0  20.0   5.0  10.0  40.0  10.0
 [9013]  10.0  40.0  10.0  10.0  21.0  10.0  10.0  10.0  10.0   2.0  10.0  26.0
 [9025]  48.0   0.6   2.0  40.0  20.0   0.6   0.7  10.0  10.0   0.6  10.0  20.0
 [9037]  37.0  36.0  40.0   7.0   0.6   6.0   0.6  10.0  10.0  39.0  10.0  10.0
 [9049]  10.0   0.3  10.0   0.6   0.6  10.0  19.0   4.0   4.0   7.0   0.6   2.0
 [9061]  10.0  40.0   4.0  18.0   1.0  10.0  10.0   0.6  10.0  10.0  40.0  40.0
 [9073]   2.0  10.0  10.0  32.0  10.0  10.0  40.0   4.0  40.0  40.0   5.0  10.0
 [9085]  20.0   6.0  10.0  20.0  20.0  10.0  40.0  10.0  35.0  44.0  10.0  20.0
 [9097]   0.9  10.0  10.0   9.0  10.0  10.0   0.6  40.0  41.0  22.0  10.0   1.0
 [9109]   0.6   2.0  10.0   2.0  10.0  10.0  10.0  10.0   2.0   1.0  20.0   0.6
 [9121]   0.6  10.0  66.0  34.0  10.0  40.0  10.0   1.0   2.0  41.0  10.0  10.0
 [9133]  20.0   0.6  10.0  41.0   1.0  40.0   0.5   0.6  10.0  20.0  10.0  10.0
 [9145]  40.0  10.0  10.0  10.0  41.0  10.0   3.0   1.0   2.0  41.0  10.0  66.0
 [9157]  12.0  35.0  41.0  40.0  10.0   2.0   0.5   0.3  10.0  10.0  10.0   0.6
 [9169]  12.0  12.0  10.0  10.0  10.0  18.0   3.0  41.0  37.0  41.0  40.0  10.0
 [9181]  10.0  40.0  10.0   0.6  20.0  10.0  40.0  10.0  10.0  10.0   0.6  41.0
 [9193]  41.0  25.0   1.0  40.0   1.0   4.0  10.0  10.0   7.0  41.0  10.0  10.0
 [9205]  10.0  20.0  19.0   6.0  10.0   0.9  18.0   0.6  40.0  10.0  10.0  20.0
 [9217]   1.0  10.0  10.0  10.0   5.0  10.0  20.0  10.0  20.0  10.0  10.0  10.0
 [9229]  10.0  40.0   0.0  10.0   2.0  10.0  20.0   0.6  10.0   4.0   0.5  10.0
 [9241]   3.0  67.0  10.0  10.0  10.0  10.0  12.0   0.6  10.0  10.0  40.0  18.0
 [9253]  20.0  10.0  20.0  20.0  10.0  20.0  41.0  10.0  10.0   1.0  10.0  19.0
 [9265]  10.0  10.0   1.0  41.0  18.0  10.0  10.0   1.0  10.0   5.0   0.5  10.0
 [9277]  10.0  10.0  10.0   0.6  30.0  67.0  40.0  10.0  10.0  10.0  10.0  64.0
 [9289]  20.0  10.0   0.6   0.6  10.0   0.6  10.0  41.0  10.0  13.0  36.0  10.0
 [9301]  15.0   2.0   3.0  10.0  20.0  10.0   0.6  10.0  10.0   2.0   7.0  10.0
 [9313]  10.0  64.0  41.0  40.0  18.0   0.6  41.0  10.0   1.0  67.0  65.0  10.0
 [9325]  10.0   8.0  10.0   2.0  40.0  10.0   2.0  39.0  10.0  10.0  20.0  40.0
 [9337]  10.0  10.0  10.0  13.0  10.0  10.0  40.0  10.0   1.0  10.0  40.0  10.0
 [9349]  20.0  10.0   8.0   0.6  20.0   0.2  10.0  10.0  65.0   5.0  13.0  68.0
 [9361]  42.0  40.0   3.0   0.6  10.0  10.0   0.2  20.0  10.0  16.0  10.0   2.0
 [9373]   2.0  10.0   2.0   9.0   0.6   0.6  52.0  40.0  10.0   0.0  10.0  10.0
 [9385]  11.0   3.0  10.0  10.0  28.0  41.0   6.0  40.0  10.0  42.0  62.0  10.0
 [9397]  10.0  10.0  10.0  10.0   0.4  10.0   0.6   1.0  10.0  40.0  40.0  20.0
 [9409]  10.0  10.0   0.0  10.0  10.0  20.0   4.0   0.6  18.0   4.0  10.0   6.0
 [9421]  10.0  40.0  36.0   0.1  42.0  10.0  10.0  14.0  12.0  10.0  20.0  10.0
 [9433]  20.0  10.0  36.0  42.0  10.0  42.0  10.0  10.0  10.0  20.0  10.0  36.0
 [9445]  42.0  68.0  20.0  10.0  20.0   4.0  20.0   4.0  10.0   0.1  11.0   0.6
 [9457]  39.0  10.0   0.6  20.0   0.6  10.0  25.0  39.0  42.0   2.0  20.0  10.0
 [9469]  19.0  19.0   2.0   2.0  10.0  10.0  11.0  12.0  11.0  10.0  10.0  11.0
 [9481]  10.0  11.0   0.6  10.0   0.6  66.0  11.0  67.0  11.0   9.0  40.0  11.0
 [9493]  11.0   8.0  11.0  42.0  10.0  10.0  11.0   0.6   2.0  20.0  20.0  33.0
 [9505]  11.0   0.7   0.6   0.6  36.0  11.0  40.0  42.0  42.0   0.5  11.0  26.0
 [9517]  11.0   0.6  11.0   0.7  11.0  42.0  11.0  20.0  41.0  11.0  11.0  11.0
 [9529]  10.0   4.0  10.0  10.0  30.0  11.0  35.0  13.0  11.0  29.0  11.0  10.0
 [9541]  67.0  20.0  11.0  41.0  11.0  10.0  36.0  42.0  42.0  11.0  10.0  11.0
 [9553]   3.0  10.0  11.0  10.0  20.0   0.6  10.0  11.0  10.0  10.0  29.0  10.0
 [9565]  14.0  25.0  10.0  10.0  11.0  11.0   0.4  11.0   0.6  10.0   3.0   0.6
 [9577]  20.0  20.0  10.0  20.0  10.0   0.6  10.0   0.6   2.0  10.0  11.0  11.0
 [9589]  10.0  10.0  11.0   0.6  10.0   2.0   7.0  11.0  28.0  11.0   1.0  10.0
 [9601]  11.0  11.0  11.0  20.0  20.0   4.0  18.0  11.0  11.0  42.0  68.0   0.2
 [9613]  42.0   0.6  10.0  11.0  10.0  11.0  11.0  10.0   3.0   4.0   0.6  36.0
 [9625]  91.0  10.0  11.0  43.0   2.0  11.0  11.0  11.0  11.0  43.0  20.0  11.0
 [9637]   1.0  11.0  11.0   0.6   2.0  68.0  11.0   9.0  11.0  68.0   0.7  35.0
 [9649]  11.0  20.0   1.0  11.0   0.6  11.0  43.0  11.0  25.0   7.0   1.0  11.0
 [9661]  68.0  20.0  11.0  11.0  13.0  36.0   7.0  11.0  11.0  41.0  20.0  41.0
 [9673]  11.0  20.0  20.0   5.0  11.0   4.0  43.0  31.0  20.0  11.0  11.0   1.0
 [9685]   3.0   2.0  20.0  20.0  41.0  18.0   0.6  11.0  11.0  11.0  11.0  11.0
 [9697]  11.0  11.0  20.0  11.0  11.0  18.0  41.0  11.0  68.0  11.0  43.0   1.0
 [9709]   0.6   0.1   0.6  18.0   0.6  11.0  20.0   0.6  11.0   0.5  11.0  11.0
 [9721]  41.0  38.0  38.0  42.0  38.0  11.0   0.5  28.0  11.0  11.0  11.0  14.0
 [9733]  11.0  11.0  43.0   7.0   0.6  11.0  20.0  20.0  11.0   0.3  41.0  31.0
 [9745]  41.0  11.0   2.0  11.0  41.0  20.0   2.0   3.0  11.0   1.0  36.0   2.0
 [9757]  43.0  11.0  24.0   9.0  11.0  11.0   9.0  18.0   0.6  95.0  11.0  11.0
 [9769]  11.0  41.0  41.0   0.8  41.0   0.6  26.0  11.0   0.6   0.6  41.0  11.0
 [9781]  11.0  11.0  20.0   0.7  18.0   1.0   0.6  11.0  41.0   0.1   0.6  11.0
 [9793]  11.0  11.0  11.0   2.0  20.0  68.0  11.0   2.0  20.0  11.0   0.6  11.0
 [9805]  11.0  43.0  43.0  41.0  11.0  11.0  36.0  11.0  35.0  43.0  11.0  43.0
 [9817]  20.0  42.0  20.0  11.0  11.0  11.0  11.0  11.0  30.0  37.0  32.0   7.0
 [9829]  11.0  19.0  11.0   2.0  11.0  11.0  11.0  11.0  37.0   0.6  43.0  42.0
 [9841]  11.0  43.0   2.0   0.6  11.0  11.0  11.0  11.0  11.0  11.0   0.6   2.0
 [9853]  11.0  36.0  43.0  42.0  11.0  24.0  42.0   4.0  11.0  11.0  42.0  11.0
 [9865]  11.0  11.0  11.0  11.0   8.0  11.0  11.0   0.6  11.0  42.0  44.0   1.0
 [9877]  11.0  11.0  42.0  11.0  11.0   0.6  11.0  20.0  20.0  42.0  11.0  14.0
 [9889]  11.0  11.0  38.0  44.0  44.0  18.0  11.0  11.0  11.0  20.0  11.0  19.0
 [9901]  11.0  11.0  13.0   0.6  11.0  44.0   3.0  42.0  42.0  11.0  11.0  11.0
 [9913]  11.0   2.0  11.0  22.0  42.0  42.0   1.0  18.0  44.0  11.0   0.6  11.0
 [9925]  44.0  11.0   2.0   0.9  40.0  40.0  43.0  40.0  11.0  42.0  11.0   0.6
 [9937]  11.0   0.1  13.0   0.5  42.0   0.6   1.0   0.9  11.0  11.0   1.0  11.0
 [9949]  11.0   2.0  11.0  11.0   0.6  11.0  11.0  11.0  44.0  11.0  11.0  11.0
 [9961]   4.0  11.0  11.0  20.0   0.6  11.0  11.0  11.0  21.0  65.0   5.0  13.0
 [9973]  11.0  11.0   6.0  11.0  44.0  20.0  21.0  38.0  32.0   3.0  38.0  11.0
 [9985]  28.0  11.0   6.0   0.6  44.0  41.0  18.0  11.0  11.0   0.6  11.0  21.0
 [9997]   0.1   0.6  20.0  11.0  42.0  20.0  11.0  42.0  33.0   0.3  69.0  11.0
[10009]  11.0  21.0  11.0  11.0   0.5  11.0  20.0  11.0  11.0   0.6  11.0  11.0
[10021]  42.0   4.0  42.0  11.0  11.0   1.0   1.0   2.0   0.7  11.0  11.0  11.0
[10033]   9.0  21.0   2.0  11.0   4.0   0.3   1.0  11.0  11.0  11.0   2.0   0.6
[10045]   0.6  44.0  42.0  11.0  11.0   3.0   0.1  11.0   1.0   9.0  11.0  11.0
[10057]  11.0  18.0  11.0  11.0  21.0  20.0  11.0  11.0  11.0  11.0  11.0   0.6
[10069]  11.0  11.0  11.0  18.0   2.0  42.0   0.6  11.0  70.0  43.0   0.6  11.0
[10081]   0.6  11.0  11.0   7.0  11.0  11.0   2.0  11.0  11.0  11.0   0.6  11.0
[10093]  21.0  70.0  37.0  11.0  43.0  11.0   0.6   4.0  43.0  11.0  17.0  44.0
[10105]   2.0  11.0   7.0  21.0  11.0  11.0  11.0   0.6  11.0  11.0   0.6  12.0
[10117]  14.0  91.0   9.0   0.7  11.0  12.0  26.0  44.0  12.0  11.0  12.0  43.0
[10129]   2.0  12.0  19.0  21.0  12.0  11.0  12.0  12.0   0.6   0.6  38.0  13.0
[10141]  13.0   0.6   1.0  49.0  12.0   0.6   9.0   4.0  11.0  12.0   0.6  12.0
[10153]   0.2  43.0  11.0   0.6  12.0  20.0  12.0  11.0  43.0  18.0  11.0  12.0
[10165]  12.0   0.6   0.3  11.0   0.6  43.0   0.6  12.0  12.0   4.0  12.0  11.0
[10177]  38.0  10.0  11.0  12.0   0.6  44.0  12.0  43.0  11.0   0.9   2.0   0.6
[10189]  18.0  11.0  12.0  12.0   5.0  11.0  70.0   0.6  21.0  37.0   7.0  13.0
[10201]  15.0  43.0   6.0   0.7  12.0  11.0  44.0  70.0  12.0  12.0  18.0  13.0
[10213]  12.0  34.0  12.0  24.0  30.0  32.0  43.0  20.0   2.0  44.0   4.0  11.0
[10225]  11.0  95.0   7.0   0.6  35.0   1.0  11.0  11.0  10.0  12.0  11.0   0.6
[10237]   0.6  11.0  43.0  44.0   1.0  70.0   0.9  21.0   7.0   4.0   5.0   0.6
[10249]   0.6  12.0  12.0   0.6  11.0  12.0  14.0  31.0  23.0  20.0  10.0   0.9
[10261]  43.0  44.0  44.0  22.0  45.0  20.0  21.0  11.0  11.0  12.0  11.0  19.0
[10273]  41.0  41.0  43.0  41.0  41.0  42.0  42.0  18.0  11.0  45.0  12.0  45.0
[10285]  71.0   0.7  11.0  12.0   2.0   8.0   0.6  11.0  44.0   2.0  45.0  11.0
[10297]   0.1   2.0  12.0  96.0  12.0  12.0  12.0  12.0  12.0  12.0  11.0   0.2
[10309]  45.0  18.0  12.0   1.0   2.0  11.0  11.0  12.0   9.0  12.0  28.0  11.0
[10321]  11.0   1.0  11.0   1.0   5.0  70.0  11.0  11.0  12.0   1.0  12.0  12.0
[10333]  41.0  12.0  12.0  11.0   2.0  43.0  11.0  13.0  97.0  12.0  29.0  12.0
[10345]  11.0  15.0  43.0  45.0  45.0  12.0  11.0  12.0   2.0  20.0  12.0  12.0
[10357]  11.0   4.0  97.0  12.0  19.0  14.0  11.0  12.0   1.0  12.0   2.0  16.0
[10369]  12.0   0.6  11.0  12.0   8.0  12.0  12.0   5.0  18.0  12.0  12.0  12.0
[10381]  12.0  12.0  12.0  12.0   9.0  12.0   1.0  12.0  12.0  12.0   2.0   0.6
[10393]   1.0   2.0  12.0  39.0   2.0  43.0  43.0   5.0  12.0  12.0  12.0  10.0
[10405]  45.0  45.0  12.0  71.0  12.0  12.0  12.0  45.0  45.0  13.0  14.0   2.0
[10417]  43.0  45.0  18.0  45.0  12.0   5.0   5.0  20.0  25.0  45.0  70.0   0.5
[10429]  12.0   0.6   0.1  14.0  12.0  43.0  21.0  12.0  43.0  12.0   0.6  14.0
[10441]  12.0   8.0  12.0  12.0  12.0  12.0  12.0  12.0  14.0  12.0  43.0  46.0
[10453]  16.0  21.0  20.0  12.0  18.0   0.9  12.0   0.6   0.6  20.0  46.0  12.0
[10465]  12.0  12.0  12.0  12.0  12.0   2.0  12.0  12.0   8.0  71.0  12.0  12.0
[10477]  12.0  12.0  43.0  46.0  43.0  43.0  30.0  21.0   6.0  12.0  98.0   4.0
[10489]  20.0  12.0  72.0  20.0  38.0  14.0   5.0  12.0   0.6  12.0  12.0  71.0
[10501]  12.0   0.0  12.0  18.0  12.0  21.0   4.0  43.0  20.0  12.0  12.0  12.0
[10513]  19.0  19.0  43.0   0.6  44.0  46.0  12.0  12.0  21.0  71.0  72.0  46.0
[10525]  44.0  20.0   0.6  12.0  72.0  14.0  46.0  46.0  46.0  12.0   2.0  12.0
[10537]  12.0  12.0  12.0  12.0   0.6  19.0   0.6  46.0   1.0  12.0  19.0  12.0
[10549]  12.0  12.0  12.0  14.0  12.0  12.0  46.0  44.0   0.2  12.0  21.0  20.0
[10561]   3.0  44.0  46.0  44.0  44.0   0.6  12.0  44.0  26.0  12.0  12.0  12.0
[10573]   3.0  12.0  20.0  46.0   0.5  18.0   5.0   0.6  12.0  12.0  72.0  12.0
[10585]  46.0  12.0  46.0  44.0  12.0  46.0   0.6  12.0  12.0  12.0   2.0   0.3
[10597]  12.0  44.0  62.0  12.0  28.0  71.0   0.7  10.0  45.0  46.0  12.0  19.0
[10609]  12.0  12.0   7.0  12.0   3.0  16.0  44.0  44.0  10.0  12.0  12.0  44.0
[10621]  12.0  12.0  71.0  12.0  12.0  12.0  43.0  12.0   0.0  12.0  12.0  44.0
[10633]  44.0  44.0   2.0   2.0  12.0  12.0  12.0   0.6  30.0  44.0   6.0  12.0
[10645]   4.0  71.0  12.0  20.0  12.0  72.0  12.0   2.0  12.0  12.0   1.0  12.0
[10657]  12.0  18.0  12.0  46.0  12.0  12.0  12.0   0.6  12.0   0.7  72.0   1.0
[10669]   0.6  39.0  14.0  12.0  12.0  12.0  12.0  15.0   0.7  44.0   0.7  12.0
[10681]   2.0  14.0  12.0  12.0  12.0  44.0  46.0  19.0   3.0   0.7  14.0  12.0
[10693]  46.0   0.7  12.0  46.0  46.0  12.0   0.6  11.0  12.0  12.0  44.0  12.0
[10705]   7.0   2.0   1.0  14.0  12.0   0.1  12.0  12.0  12.0   1.0   0.6   0.7
[10717]  21.0  12.0  12.0  12.0   0.8   8.0  12.0   0.6  12.0   5.0   2.0   4.0
[10729]  12.0  12.0  44.0  20.0  12.0   0.5  20.0   2.0  12.0  44.0  43.0  44.0
[10741]   0.6   0.3  12.0   2.0  44.0   3.0   9.0  73.0  12.0  38.0  12.0  12.0
[10753]  46.0  12.0  12.0   9.0  12.0  21.0  12.0  39.0   0.6  13.0   0.7   0.7
[10765]  12.0  12.0  20.0  47.0  13.0   8.0  47.0  20.0   2.0  13.0   6.0  13.0
[10777]  21.0   0.7  24.0  13.0   2.0   0.7  13.0   0.7  13.0  12.0  12.0  18.0
[10789]   0.7   4.0   1.0  44.0  12.0  13.0   0.7  12.0  72.0  13.0   1.0  39.0
[10801]   5.0  21.0  72.0  21.0   4.0  12.0  36.0  13.0  13.0  13.0  45.0  12.0
[10813]  12.0  13.0   2.0   9.0  12.0  12.0  15.0  13.0  12.0  45.0  12.0  13.0
[10825]  13.0  21.0  46.0  12.0   2.0  12.0  14.0  12.0  14.0   1.0  21.0  13.0
[10837]  39.0   4.0  12.0  13.0  44.0  44.0  44.0   2.0  19.0   2.0  65.0  42.0
[10849]  47.0  13.0   0.7  72.0  45.0   0.3   1.0  12.0   0.7  45.0  47.0  42.0
[10861]   1.0  73.0   0.7   0.9  18.0   0.7   0.7  13.0  12.0  12.0  10.0  39.0
[10873]   1.0  45.0   0.0   0.1   0.7   9.0  12.0  12.0  12.0   0.7  12.0  39.0
[10885]  12.0  31.0  47.0  12.0  21.0  12.0  13.0  12.0  73.0   0.5  13.0   0.7
[10897]   2.0  13.0  13.0  47.0  45.0  21.0   5.0  13.0  18.0  47.0  21.0  12.0
[10909]  12.0   0.1   1.0   1.0  45.0  27.0  13.0  45.0   0.7  18.0  12.0  23.0
[10921]  12.0  13.0  21.0  13.0  45.0  13.0  47.0  13.0  13.0  12.0   5.0  13.0
[10933]  12.0   0.7  47.0  45.0  20.0  13.0  73.0   0.7  21.0  12.0   0.7   0.7
[10945]   4.0  12.0  13.0  45.0  18.0   0.2  13.0  12.0   5.0  13.0  13.0  12.0
[10957]  12.0  12.0  13.0  22.0  12.0   4.0  21.0  14.0  15.0   1.0  45.0  47.0
[10969]  13.0   5.0  47.0  30.0   4.0   0.7   0.9  21.0  12.0  13.0   5.0  27.0
[10981]  15.0  12.0  35.0  45.0  45.0  47.0  47.0  14.0   0.2  45.0  12.0  12.0
[10993]  53.0  12.0  12.0   2.0  45.0  12.0  12.0  12.0  13.0  12.0  39.0  24.0
[11005]  10.0  45.0  45.0  13.0  13.0  13.0  74.0  12.0  19.0   2.0  45.0  22.0
[11017]  13.0   2.0  10.0  12.0  13.0  74.0  12.0  18.0  14.0  19.0   2.0   7.0
[11029]  12.0   1.0  47.0   1.0  18.0  47.0  12.0  74.0  12.0  13.0   0.3   0.7
[11041]  13.0  12.0   0.7  18.0  13.0  13.0   0.9   0.7   0.7  54.0  13.0  12.0
[11053]  12.0   2.0  45.0   0.7  12.0  46.0  22.0   2.0  75.0  12.0   8.0  39.0
[11065]  13.0   0.7  46.0  13.0  22.0  12.0  12.0  13.0   2.0  12.0  12.0  46.0
[11077]   3.0  21.0  21.0  13.0  21.0  13.0  72.0  46.0  47.0  46.0  12.0   0.7
[11089]   0.7   2.0   0.7   0.7  13.0  46.0  12.0  13.0  13.0  13.0  13.0   0.7
[11101]  75.0  13.0  12.0  47.0  13.0  48.0  48.0  48.0  38.0  13.0   5.0  13.0
[11113]  13.0  13.0  13.0  13.0   1.0  13.0  39.0  14.0  48.0  46.0  46.0   0.3
[11125]  21.0  13.0  13.0   0.7  13.0   0.7   0.9   0.7   9.0  13.0  22.0  40.0
[11137]  66.0  32.0   3.0  37.0  13.0  13.0   1.0  20.0  13.0   0.0  13.0  22.0
[11149]  13.0  13.0  13.0  47.0  47.0   0.7   0.1  33.0  13.0  48.0  13.0   0.7
[11161]  21.0  13.0  39.0  13.0  13.0  47.0  73.0   9.0  13.0  13.0  13.0   0.7
[11173]  13.0  26.0  13.0  13.0  13.0   1.0   0.2  13.0   0.8   0.7   0.7  47.0
[11185]  47.0  13.0   2.0   0.7  13.0  13.0  48.0  47.0  13.0  13.0   2.0  18.0
[11197]  13.0   1.0   4.0  21.0  13.0  13.0  48.0   0.7   4.0   0.7  18.0  13.0
[11209]   2.0  13.0   0.7  13.0  48.0  48.0  13.0  22.0  11.0   0.1  47.0   0.7
[11221]  76.0  13.0   3.0  13.0  21.0  20.0  15.0  35.0  48.0  13.0  14.0  48.0
[11233]  13.0  13.0  22.0   0.8   6.0  11.0  13.0  13.0  47.0  13.0  22.0   6.0
[11245]  27.0  39.0  13.0  77.0  48.0  13.0  14.0   3.0  13.0  13.0  21.0  13.0
[11257]  13.0  13.0  20.0  13.0   0.7  13.0  35.0   0.7  48.0  13.0   0.7   6.0
[11269]   4.0   2.0   5.0  13.0  13.0  13.0  13.0  13.0  13.0  39.0  11.0  13.0
[11281]  48.0  48.0   0.4  18.0   0.7  22.0  13.0  15.0  16.0  47.0   3.0   2.0
[11293]   7.0   9.0   0.7  40.0  14.0  45.0  24.0  44.0  44.0  44.0  49.0  13.0
[11305]   0.7   3.0  13.0  49.0  46.0  49.0  18.0  47.0  13.0  13.0  13.0  13.0
[11317]   0.3   3.0  13.0  19.0  49.0  13.0  13.0   3.0  13.0  18.0  13.0  21.0
[11329]  48.0  13.0  21.0  13.0  13.0   1.0  13.0  21.0  13.0  13.0   2.0  13.0
[11341]   0.7  13.0  13.0  13.0  77.0  21.0  21.0  49.0  21.0   1.0  48.0  48.0
[11353]  13.0   0.6   0.7  77.0   0.7  40.0  13.0   2.0  19.0  48.0   2.0  13.0
[11365]  13.0  13.0  13.0  13.0  73.0   1.0   0.7  22.0  13.0   1.0  13.0  48.0
[11377]  13.0  13.0  13.0   2.0   1.0  29.0  18.0  13.0  22.0  13.0  13.0  13.0
[11389]  22.0  14.0  15.0  49.0  49.0  49.0   3.0  13.0  10.0  13.0  13.0  13.0
[11401]  13.0   0.7   4.0  13.0  13.0   0.5  18.0  13.0  48.0   5.0  13.0   0.6
[11413]  13.0  13.0  13.0  13.0   0.2   6.0  13.0   9.0  42.0  15.0  49.0  18.0
[11425]   3.0  49.0   4.0   4.0  49.0   0.5   7.0  19.0  13.0   0.7  13.0   0.2
[11437]  12.0  22.0  13.0  13.0   0.7  49.0  78.0   0.8  21.0  13.0  22.0  20.0
[11449]   1.0  21.0  48.0  13.0  13.0  21.0   0.3  13.0  73.0  21.0   0.7  34.0
[11461]  48.0   4.0  13.0  49.0   5.0  48.0  49.0   3.0  13.0  13.0   0.7  13.0
[11473]   7.0   2.0   2.0  21.0  13.0  13.0  49.0  34.0  33.0  49.0  48.0   1.0
[11485]   0.6   9.0   2.0   0.7  13.0  48.0   0.5  13.0  13.0  18.0  13.0  49.0
[11497]   0.7   2.0  13.0   2.0   8.0  12.0  13.0  13.0  48.0   4.0  13.0  48.0
[11509]  30.0   4.0   3.0  48.0   0.7   0.0   2.0  49.0   0.7  13.0   0.7   0.7
[11521]  13.0  12.0   0.7  15.0  18.0  48.0  13.0  18.0  49.0  13.0  13.0  13.0
[11533]  13.0  49.0   0.7   3.0   2.0  13.0  21.0  13.0  14.0   0.7  13.0  13.0
[11545]  13.0   4.0  49.0  31.0   0.5   0.7  13.0   0.7  13.0  13.0  21.0  14.0
[11557]  14.0  14.0   0.3  27.0  32.0  19.0  48.0  49.0  14.0   0.7  14.0  49.0
[11569]  13.0   0.0   4.0   1.0   5.0   0.7  14.0   0.7  18.0   0.7  14.0  13.0
[11581]  49.0  13.0   9.0  13.0  20.0  73.0  14.0  14.0   0.8   2.0  14.0  13.0
[11593]   0.7   0.7  14.0  20.0  15.0  44.0  22.0  44.0  49.0  14.0   9.0  13.0
[11605]  14.0  13.0  15.0  29.0  14.0   0.7  14.0  19.0  14.0  21.0  13.0  50.0
[11617]   0.7   1.0   0.7  13.0   0.7  13.0  55.0   0.5   0.7   0.7  67.0  13.0
[11629]  50.0  14.0  22.0  21.0  14.0  13.0  49.0  50.0   5.0  49.0  13.0  22.0
[11641]  98.0   0.7  15.0   2.0  14.0  34.0  22.0  13.0  50.0  13.0  10.0   1.0
[11653]  14.0  28.0  13.0  14.0   2.0  14.0  14.0  13.0  13.0  49.0  50.0  13.0
[11665]   0.0  14.0  49.0  13.0  14.0  73.0  15.0  18.0  14.0  13.0  14.0  13.0
[11677]  50.0  49.0  50.0   1.0  12.0  49.0  14.0  13.0  15.0  15.0  18.0   0.7
[11689]  49.0   1.0  13.0  19.0   0.7   1.0  13.0  32.0   0.7  13.0  13.0  13.0
[11701]   2.0  13.0  38.0  14.0  22.0  13.0  14.0  13.0  13.0  13.0  13.0   7.0
[11713]   1.0  18.0   4.0   1.0   0.7  13.0  14.0   0.7  13.0   0.7   0.7   0.7
[11725]  13.0   1.0  41.0  50.0   0.0   6.0   0.7  13.0   2.0  22.0  13.0  22.0
[11737]  19.0  49.0   4.0  14.0  14.0  12.0  11.0  14.0  14.0  13.0  13.0  14.0
[11749]  50.0  13.0   1.0  18.0  50.0  14.0  50.0  12.0  14.0  91.0  44.0  45.0
[11761] 119.0  52.0  18.0  13.0  50.0  13.0   1.0   0.7  14.0   0.7  14.0  16.0
[11773]  50.0  17.0  50.0  14.0  20.0  13.0   0.8  14.0  13.0  13.0   0.7   3.0
[11785]  13.0  13.0  13.0  13.0  14.0  14.0  14.0  14.0  13.0  14.0  45.0  29.0
[11797]  50.0   0.7  13.0   0.4  22.0   6.0  13.0  14.0   1.0  22.0  13.0  71.0
[11809]   0.7  13.0   1.0   0.9   0.5  13.0  13.0  13.0  13.0  15.0  28.0  18.0
[11821]  50.0  14.0  14.0   2.0  74.0  14.0   0.3  14.0  50.0  14.0  50.0  13.0
[11833]  13.0  14.0  15.0  50.0  51.0  14.0   0.3  13.0  50.0  74.0   0.7  13.0
[11845]  50.0  50.0   2.0  13.0  14.0  50.0  14.0   1.0  14.0   0.7  14.0  51.0
[11857]  34.0   4.0  50.0   9.0   3.0   6.0   0.7  51.0  14.0  24.0   2.0  13.0
[11869]  13.0  14.0  13.0   1.0  51.0  51.0   1.0  14.0  13.0  75.0  13.0  14.0
[11881]  13.0  14.0  15.0  62.0   0.5  75.0   0.7  13.0   3.0  22.0  13.0  14.0
[11893]  12.0  50.0   3.0  50.0  14.0  14.0  14.0  14.0  98.0  14.0   0.9  67.0
[11905]  15.0  14.0  14.0  20.0  14.0  14.0  16.0  38.0  58.0  41.0  44.0  22.0
[11917]  14.0  50.0   4.0   0.3  14.0  51.0  12.0  51.0  50.0  51.0   0.7  14.0
[11929]  14.0   1.0  14.0   2.0   5.0  17.0  14.0  14.0  51.0  14.0  74.0  10.0
[11941]   4.0  22.0  14.0  14.0  50.0  51.0   0.7  50.0  14.0  14.0  14.0  14.0
[11953]  14.0   0.7  14.0  11.0   1.0  14.0  14.0  43.0  16.0   9.0  51.0   2.0
[11965]  14.0  14.0  75.0   4.0  14.0  14.0  14.0  42.0  14.0  51.0   0.7   3.0
[11977]   0.7  14.0  14.0   2.0  68.0  10.0  12.0   0.7  14.0  50.0  18.0  14.0
[11989]  14.0  14.0  22.0  14.0  14.0  14.0   0.7  79.0  14.0  14.0   2.0  14.0
[12001]   0.3  51.0  13.0  14.0   0.7  14.0   0.7   0.7  51.0  75.0   0.7   5.0
[12013]  77.0   9.0  14.0  51.0  22.0  12.0  18.0   1.0  14.0   3.0   1.0  19.0
[12025]  14.0  14.0  14.0   2.0  51.0  32.0  14.0  14.0  14.0  22.0  14.0  20.0
[12037]  43.0  14.0   2.0  16.0  14.0  14.0   1.0  14.0  52.0  14.0   1.0   0.7
[12049]  18.0   2.0   4.0   0.7  14.0  14.0  18.0  14.0   2.0   0.7  16.0   0.7
[12061]  14.0  45.0  14.0   4.0  14.0  14.0  79.0  14.0   6.0  14.0  50.0  14.0
[12073]  50.0  19.0   2.0  14.0  23.0  13.0   0.7  14.0  42.0  16.0  14.0  14.0
[12085]  14.0  16.0  14.0  14.0  14.0   6.0   1.0  50.0   0.7  14.0  46.0   0.7
[12097]  10.0   1.0   1.0  14.0   6.0  14.0  51.0   0.7  52.0  14.0  14.0  14.0
[12109]  52.0   0.8  51.0  14.0  22.0  14.0  14.0  14.0  10.0  77.0  14.0  25.0
[12121]   0.7  31.0  51.0   0.7   6.0   0.7   6.0  14.0  14.0   0.7  14.0   4.0
[12133]  14.0  52.0  22.0  52.0  14.0  14.0   0.2  22.0  14.0  14.0  14.0  14.0
[12145]  14.0   0.7   9.0   0.3   0.7  22.0  37.0   0.0  14.0   0.9  23.0  80.0
[12157]  14.0  23.0   1.0  15.0  14.0  52.0  14.0  51.0  14.0  14.0  81.0  14.0
[12169]   2.0  14.0  14.0  14.0  14.0   4.0  14.0   0.7  14.0   0.7   4.0  14.0
[12181]   1.0   0.7  23.0  72.0   1.0  14.0  22.0  14.0  22.0  11.0  29.0   2.0
[12193]  51.0  14.0  14.0  14.0  14.0  23.0   0.7  14.0  51.0  14.0  23.0  14.0
[12205]  51.0  14.0  14.0  14.0   0.4  14.0  22.0  23.0  42.0  20.0   0.0  43.0
[12217]  14.0  51.0   0.7  14.0  14.0  14.0   0.7  14.0   1.0   0.7   7.0  81.0
[12229]   2.0  14.0  14.0  14.0   2.0  22.0  77.0  14.0  14.0  52.0  38.0  16.0
[12241]   2.0  52.0  14.0   0.7  14.0  10.0   9.0   7.0  14.0  14.0  14.0  12.0
[12253]   1.0  14.0  47.0  47.0  51.0   2.0  14.0  14.0  14.0  14.0  14.0   1.0
[12265]  14.0  22.0  46.0  45.0  46.0  47.0  77.0  14.0  14.0  51.0  52.0  14.0
[12277]  23.0  22.0  14.0  14.0  14.0  14.0  14.0   0.9  14.0  13.0  14.0  22.0
[12289]   1.0   0.7  14.0  14.0  14.0  14.0  10.0  14.0  14.0  14.0  52.0  11.0
[12301]   0.7  52.0  14.0  39.0  14.0  13.0   0.5  14.0  14.0  53.0   0.7  51.0
[12313]  14.0  51.0   3.0   0.8  14.0  14.0  22.0   0.1  14.0   0.7  14.0  14.0
[12325]  15.0  14.0  14.0   2.0   0.6  14.0  28.0  14.0  15.0  14.0  14.0  28.0
[12337]   0.0  53.0  14.0  10.0  14.0  23.0   0.8  18.0  59.0  14.0  14.0  14.0
[12349]  51.0  28.0   2.0  53.0   0.3  14.0   0.7  14.0  14.0   3.0  20.0  64.0
[12361]   2.0  23.0  53.0  14.0  14.0   0.8   0.7  14.0  14.0  14.0  44.0  15.0
[12373]  53.0  14.0  14.0   1.0   0.1  14.0  14.0  18.0  14.0  14.0  14.0  14.0
[12385]  14.0  14.0  82.0   6.0   1.0   0.8   0.8  53.0  14.0  14.0  14.0  14.0
[12397]  14.0  14.0  21.0  12.0  14.0  53.0  22.0  37.0  23.0  14.0  53.0   4.0
[12409]  14.0  14.0  14.0  23.0  23.0  14.0  14.0  14.0  46.0  35.0   1.0   1.0
[12421]   0.8   0.8  14.0   0.7  14.0  14.0  10.0   0.7  18.0  14.0  14.0  14.0
[12433]  53.0  14.0  23.0  14.0   1.0  77.0   1.0  14.0   0.8  13.0  44.0  14.0
[12445]  51.0  23.0  14.0   0.7   7.0  53.0  23.0  14.0  14.0   0.7  14.0  14.0
[12457]  14.0  51.0  14.0  14.0   0.8  14.0   6.0  10.0  14.0   6.0  11.0   2.0
[12469]  14.0  14.0   0.7   0.7   0.8   0.8  43.0   7.0  20.0   5.0  14.0  53.0
[12481]  53.0  53.0   0.7  11.0   2.0   0.0  14.0  53.0  13.0   6.0  14.0   7.0
[12493]   0.8  16.0  47.0  47.0  47.0  45.0  45.0  15.0  53.0   0.8  54.0  52.0
[12505]  25.0  17.0  54.0  14.0  12.0  18.0   0.8   2.0  14.0  21.0  15.0  52.0
[12517]  14.0  15.0   0.7   0.8  15.0  14.0   0.8  14.0   2.0  15.0  15.0   4.0
[12529]  15.0  11.0  52.0  52.0  14.0  14.0   2.0  14.0  15.0  14.0  52.0   0.0
[12541]  14.0  14.0  14.0   4.0  82.0  14.0  14.0   6.0   0.8   2.0  15.0  15.0
[12553]  14.0   0.3  15.0  15.0  15.0   0.8  78.0  18.0   0.3   0.8  14.0   1.0
[12565]  14.0  14.0  24.0  15.0   2.0   0.7  52.0  77.0   7.0  52.0  52.0  18.0
[12577]  18.0  23.0  54.0   0.8  15.0  17.0  15.0   0.3  15.0  14.0  16.0  15.0
[12589]  52.0  15.0   2.0  15.0  15.0   0.8  54.0   4.0  15.0   0.5  15.0  15.0
[12601]  15.0   0.4  52.0  54.0  28.0   1.0  54.0  15.0  15.0   0.8  26.0  15.0
[12613]   2.0  52.0  37.0   1.0  15.0  15.0  15.0  15.0  46.0  38.0  15.0   2.0
[12625]  15.0  15.0   0.7  15.0  27.0  15.0  15.0  24.0  15.0   2.0  15.0  29.0
[12637]  54.0  52.0  54.0   0.7  15.0   1.0  23.0  15.0  15.0  15.0   0.8  15.0
[12649]   4.0  15.0  15.0   0.0  20.0   1.0   9.0   2.0   0.8   2.0   0.8   0.8
[12661]  47.0  15.0  52.0  15.0  15.0   0.8  15.0  44.0  31.0  16.0  48.0  33.0
[12673]  82.0   0.8  15.0  52.0  54.0  18.0  15.0   5.0  15.0  15.0  15.0  14.0
[12685]   0.1   0.8   0.6  16.0   1.0   0.3   0.0  54.0  13.0   1.0   6.0   1.0
[12697]   1.0  15.0  15.0  31.0  15.0   0.8   0.8  91.0  81.0  18.0   0.8   1.0
[12709]   5.0  34.0  54.0  52.0   0.8  15.0   1.0  15.0   2.0  15.0  15.0  12.0
[12721]  15.0  15.0  23.0   0.8  15.0   1.0  24.0  15.0  15.0  15.0  15.0  18.0
[12733]  15.0  48.0  16.0   0.8  15.0  15.0  23.0   0.8   0.8  53.0  15.0   2.0
[12745]  50.0  15.0  53.0  15.0  53.0   0.8   0.8  54.0  15.0  24.0   1.0  53.0
[12757]   2.0  15.0  15.0  24.0   0.8  25.0  53.0  15.0   0.8  53.0  15.0  15.0
[12769]  15.0  15.0  15.0  15.0  38.0   0.8  78.0  15.0  15.0  15.0  24.0   8.0
[12781]  15.0   0.8  15.0  15.0   4.0  15.0  14.0  15.0   0.8  54.0  53.0  53.0
[12793]  20.0  15.0   0.0   0.3  37.0  15.0  15.0   0.8  24.0   0.8  24.0   7.0
[12805]  15.0  15.0  15.0   0.0  24.0   0.8  15.0  15.0  53.0  15.0  30.0  15.0
[12817]  15.0  24.0   0.8  15.0  15.0  15.0  11.0  15.0   9.0  15.0  11.0  15.0
[12829]  30.0  11.0  32.0   2.0  15.0   0.3  15.0  15.0  53.0   0.8  13.0   5.0
[12841]   0.8  15.0  15.0  16.0   4.0  24.0  53.0  27.0   4.0   3.0  26.0  14.0
[12853]  15.0  82.0  15.0  15.0  15.0   2.0  20.0  15.0   0.8  78.0  24.0  10.0
[12865]  15.0   0.8  53.0  24.0   0.5  24.0  11.0  24.0  15.0  24.0  18.0  15.0
[12877]  10.0  15.0  15.0  15.0   2.0   0.8  16.0  24.0  15.0  54.0   1.0   0.8
[12889]   0.8   2.0  15.0   4.0  15.0  14.0  24.0   0.9  15.0  15.0  15.0  15.0
[12901]  15.0  15.0  16.0  15.0  15.0  15.0   2.0  23.0   0.5  16.0  53.0  15.0
[12913]  15.0  24.0   0.8  19.0   0.8  15.0  53.0   0.8  15.0   1.0   6.0  15.0
[12925]  55.0  14.0  55.0  15.0  18.0  15.0  35.0  15.0   0.4   0.1   5.0   0.2
[12937]   0.4  15.0  15.0  53.0   1.0   0.8  25.0  78.0  18.0   4.0  15.0  15.0
[12949]  15.0  15.0  15.0   0.8  47.0  48.0  49.0  49.0   4.0  25.0   0.3  12.0
[12961]   2.0  52.0   3.0   0.4  15.0   2.0  83.0  18.0  14.0  48.0  31.0  55.0
[12973]  11.0  55.0   0.8  15.0   0.8   0.1  54.0   0.6  55.0  55.0  15.0   0.8
[12985]  15.0   0.8   0.4  15.0   0.6  15.0  15.0  52.0  55.0  15.0  55.0  55.0
[12997]  24.0   0.8  15.0  16.0  15.0  15.0   0.1  15.0  15.0  12.0  13.0  15.0
[13009]   2.0  16.0   2.0  15.0   6.0  78.0  78.0  25.0   6.0  15.0  55.0  54.0
[13021]  55.0   0.0   4.0  54.0  55.0  54.0  15.0  15.0  15.0  13.0  15.0  98.0
[13033]  15.0  15.0  15.0   0.8  79.0  15.0  15.0   1.0  25.0  18.0  15.0  99.0
[13045]  80.0   0.8   2.0  54.0  38.0   0.8  25.0  15.0  15.0  41.0  15.0  54.0
[13057]  15.0  55.0  15.0  15.0   2.0  25.0   1.0   6.0  15.0  15.0  54.0  55.0
[13069]   0.4  55.0  15.0  14.0  12.0   4.0   7.0  56.0  15.0  18.0  15.0  15.0
[13081]  84.0  56.0  13.0  20.0  27.0  15.0  54.0   1.0  15.0  15.0  15.0  15.0
[13093]  49.0  49.0  49.0  15.0   1.0   0.8  15.0  15.0   0.6  15.0  26.0  56.0
[13105]   2.0  54.0   1.0  15.0  15.0  25.0   1.0  80.0   1.0   1.0  54.0  26.0
[13117]   2.0  25.0  15.0  15.0   0.6  15.0  15.0  15.0  15.0  26.0   0.8  54.0
[13129]  54.0  15.0  15.0  15.0  15.0  15.0   0.8   2.0  56.0  54.0  15.0  56.0
[13141]  15.0  15.0  15.0  15.0   0.8  12.0  52.0  26.0  15.0  56.0  15.0  15.0
[13153]  54.0   1.0  14.0  18.0  15.0  56.0   2.0  15.0   6.0  55.0  15.0   3.0
[13165]  53.0  15.0   0.0  15.0  15.0  55.0   1.0  26.0  15.0  15.0  84.0  15.0
[13177]  11.0  26.0  15.0  27.0  15.0   6.0   2.0  13.0  56.0  56.0   0.8  10.0
[13189]  18.0  12.0   0.8   1.0  15.0  15.0  15.0  19.0   0.8   4.0  15.0  15.0
[13201]  15.0  54.0  54.0  80.0  55.0  15.0  13.0   0.8  16.0  20.0  15.0   6.0
[13213]   4.0   1.0   2.0  15.0  15.0  16.0  15.0  27.0  15.0  15.0  15.0   1.0
[13225]  15.0  15.0  15.0  72.0  29.0   6.0  15.0  15.0  55.0   3.0  16.0  15.0
[13237]  44.0  50.0   0.8  15.0  15.0   0.8  56.0  15.0  15.0  15.0  15.0   5.0
[13249]   0.7  15.0  54.0  16.0   6.0  31.0  55.0  11.0   1.0   0.8  80.0  27.0
[13261]   3.0  56.0   0.2  15.0   6.0   1.0  56.0   2.0  15.0   0.8   1.0  15.0
[13273]  12.0  15.0  10.0  56.0  15.0  19.0  12.0  81.0   0.8  20.0  16.0   0.8
[13285]   0.8  10.0  15.0   0.8  15.0  15.0  55.0  27.0  14.0  27.0  15.0  55.0
[13297]  15.0   2.0  19.0  15.0   1.0  15.0   0.8  15.0  15.0  15.0   0.8   0.6
[13309]  27.0  15.0   0.8  15.0  55.0  28.0   1.0  50.0   0.8  57.0   5.0   2.0
[13321]  81.0  15.0   0.8  15.0  15.0  15.0  30.0  55.0  15.0   2.0   7.0   3.0
[13333]  15.0  28.0  57.0   2.0  15.0  15.0  15.0   4.0  55.0  15.0  15.0  15.0
[13345]  15.0  15.0  15.0  15.0   0.8  15.0  57.0   1.0  57.0   0.8  15.0  15.0
[13357]  15.0   6.0  15.0  20.0   1.0   1.0   0.6   2.0   7.0  15.0  15.0  55.0
[13369]  15.0  57.0  15.0  15.0  28.0   0.8  41.0  35.0  50.0  15.0  55.0   6.0
[13381]  15.0  15.0  15.0  15.0   2.0  55.0  15.0  15.0  34.0  15.0   2.0   0.1
[13393]   4.0  56.0   5.0  15.0  22.0  15.0  15.0  15.0  15.0  17.0  15.0  15.0
[13405]  15.0   0.8   0.8  15.0  15.0  15.0  15.0   0.0  15.0  15.0  15.0  15.0
[13417]  15.0  32.0  15.0  15.0  56.0  19.0  19.0  15.0  15.0  57.0  15.0  15.0
[13429]   0.8  16.0  28.0  29.0  15.0   4.0  18.0  56.0  15.0  14.0  14.0  28.0
[13441]   1.0  15.0  28.0  15.0  15.0  56.0  15.0  15.0  28.0  16.0  17.0  15.0
[13453]  56.0  36.0  15.0   2.0  14.0   4.0  15.0  28.0  15.0  16.0  15.0  30.0
[13465]  11.0  15.0  15.0  57.0   0.7  28.0  29.0  15.0  15.0   2.0   0.8  30.0
[13477]  15.0  15.0   1.0  43.0   1.0   0.2   0.8   3.0  15.0  15.0  15.0  15.0
[13489]   0.8  15.0  15.0   2.0  29.0  57.0  16.0  29.0  15.0   6.0  15.0  18.0
[13501]   2.0  15.0   2.0  20.0  16.0   5.0  57.0  15.0  44.0  13.0   0.8  15.0
[13513]  15.0  56.0  34.0  15.0   2.0  57.0  15.0   0.8   0.8  15.0  15.0   6.0
[13525]   2.0  15.0  57.0   0.8   0.8  27.0  15.0  15.0   0.7  53.0  15.0   2.0
[13537]   9.0   0.9   0.8  15.0  15.0  18.0  15.0  20.0  18.0  15.0  15.0   0.8
[13549]  16.0   0.8  16.0  15.0  28.0   0.8   9.0  85.0  15.0  28.0   1.0  29.0
[13561]  15.0  23.0  29.0   3.0  15.0  18.0   8.0  15.0  28.0  16.0   0.6   0.8
[13573]   0.8  15.0  16.0  50.0  50.0  51.0  51.0  60.0  16.0  16.0   6.0   0.8
[13585]  57.0  17.0   4.0  16.0   0.8  16.0   1.0  20.0  16.0  16.0  29.0  15.0
[13597]  15.0   0.8   0.8   5.0   0.6  15.0  15.0  30.0   4.0  15.0   0.1   0.8
[13609]  13.0  15.0   1.0   1.0   2.0  16.0  16.0   8.0  16.0  16.0  57.0  57.0
[13621]  57.0   0.6  16.0  57.0  29.0   2.0  16.0   1.0  16.0  16.0  29.0  16.0
[13633]  16.0  16.0   5.0  57.0   0.9   0.8  16.0  16.0  16.0  50.0  16.0  16.0
[13645]   0.8  16.0  57.0  57.0  16.0  16.0  16.0  16.0  81.0  72.0   0.8  16.0
[13657]  16.0   0.8  16.0   2.0   0.8  25.0  16.0  16.0  57.0   1.0  16.0  63.0
[13669]  16.0  29.0   1.0  16.0   0.0  16.0  16.0   2.0  16.0  16.0   0.8   0.7
[13681]  57.0  16.0  57.0   2.0  85.0  57.0  16.0  57.0   0.8  30.0  16.0  16.0
[13693]  85.0  16.0  16.0  16.0  16.0  22.0  19.0  16.0  16.0  16.0   0.8   1.0
[13705]  12.0  14.0  16.0   5.0  16.0  16.0   6.0  16.0   0.8  57.0  58.0  57.0
[13717]  16.0   5.0  16.0  16.0  18.0  29.0  16.0  16.0  16.0  16.0  20.0  54.0
[13729]   0.8  27.0  57.0   0.8   2.0  57.0  16.0  16.0  16.0   1.0   2.0  16.0
[13741]  16.0   0.5  55.0  16.0  40.0   0.8  57.0  58.0  16.0  16.0  58.0  16.0
[13753]  29.0   1.0  18.0  30.0   0.8   0.8  55.0  55.0  16.0  14.0  16.0  58.0
[13765]   0.9  30.0  16.0  16.0  14.0   0.8  16.0  55.0   6.0  17.0  26.0  58.0
[13777]   2.0   0.8  16.0  30.0  16.0   2.0  16.0  16.0  16.0  16.0   0.5  29.0
[13789]  16.0  16.0  16.0   1.0  57.0   0.8  16.0  16.0  85.0  16.0  16.0  58.0
[13801]  58.0   1.0  16.0   0.8  16.0  58.0   0.8  16.0  16.0  16.0  16.0  16.0
[13813]   1.0  51.0  53.0  33.0  58.0  16.0  81.0  15.0  16.0   2.0   6.0  58.0
[13825]   0.8  16.0  16.0  16.0  16.0  30.0   4.0   0.9  16.0  58.0  16.0  40.0
[13837]  58.0  16.0   0.8  16.0  16.0   0.8  58.0  17.0  21.0   0.8  24.0  16.0
[13849]  12.0   1.0   8.0  16.0  16.0   0.8  30.0   5.0  45.0  16.0  16.0  16.0
[13861]   0.8  16.0   0.3  58.0  30.0   0.2   0.8  16.0  58.0   1.0   0.8  85.0
[13873]   2.0  16.0  16.0  16.0   7.0  11.0   2.0  16.0  16.0  16.0   0.8  58.0
[13885]  16.0  82.0  30.0  58.0   0.4  16.0  58.0  30.0   2.0  16.0  18.0  16.0
[13897]  16.0  16.0  16.0  16.0  58.0   2.0   0.8  16.0  16.0  16.0  84.0  16.0
[13909]   5.0  20.0  16.0  16.0   0.6   1.0   0.8  16.0  18.0   9.0  14.0   1.0
[13921]  29.0   1.0   0.9  58.0   1.0  36.0  16.0  31.0   0.8   5.0  16.0  16.0
[13933]  16.0   0.8  16.0   0.8   0.9   0.5  10.0  58.0  68.0   2.0  16.0  16.0
[13945]  16.0  30.0  16.0  16.0  16.0  56.0   5.0  47.0   1.0  58.0   2.0  16.0
[13957]   6.0  16.0  31.0  91.0  58.0  59.0  58.0   2.0  16.0   7.0  16.0  16.0
[13969]  16.0  16.0   0.9  58.0  35.0   0.2   0.8  86.0  16.0  31.0  16.0   0.5
[13981]  16.0  81.0  32.0  59.0  45.0   7.0  59.0  16.0  15.0   2.0   2.0  16.0
[13993]  16.0   3.0   0.8  16.0  57.0   0.9   0.8   4.0  82.0   2.0  16.0  16.0
[14005]  16.0  17.0  57.0  58.0  16.0  16.0  58.0  16.0  16.0  16.0   0.9  16.0
[14017]  16.0  82.0   3.0  21.0  53.0  51.0  53.0  54.0   1.0  59.0   1.0  58.0
[14029]   8.0  10.0   0.8  16.0  35.0  16.0  16.0   0.9  52.0  16.0  16.0  16.0
[14041]  16.0  16.0  16.0   5.0  16.0   8.0  16.0   6.0  16.0  16.0  59.0  17.0
[14053]   0.3  58.0  16.0  16.0  58.0  58.0  16.0  16.0  14.0   1.0  84.0  16.0
[14065]  16.0  16.0  16.0   0.7   0.8  16.0  31.0  59.0  19.0   0.9  59.0  16.0
[14077]  16.0   1.0   6.0  31.0  59.0   0.8  59.0  59.0  59.0  59.0  59.0  32.0
[14089]  59.0  16.0  16.0  30.0  16.0   2.0   0.8  16.0  16.0  16.0  16.0  16.0
[14101]  14.0  16.0  16.0  59.0  16.0   0.8  59.0  19.0  10.0  30.0  11.0   1.0
[14113]  16.0  30.0  16.0  14.0  16.0  59.0  16.0  16.0   2.0   1.0   0.9  10.0
[14125]   4.0   0.9   6.0  16.0  16.0  30.0  50.0  15.0   9.0   9.0   2.0   1.0
[14137]  16.0  59.0   2.0  16.0  16.0   6.0   0.9  16.0   0.2  37.0  16.0  59.0
[14149]  60.0   0.9  13.0   9.0  16.0   0.9   0.4  16.0  16.0   6.0   6.0  16.0
[14161]   0.9  13.0  16.0  17.0   0.3  86.0  60.0  16.0  16.0  16.0  82.0   0.9
[14173]  16.0  16.0  32.0   0.9  32.0  82.0  60.0  54.0  16.0  16.0   9.0  16.0
[14185]   4.0  60.0  16.0   2.0  60.0  16.0  60.0  60.0  60.0  60.0  30.0  31.0
[14197]  16.0   0.8  16.0  16.0  14.0  16.0  16.0  16.0  60.0  16.0  59.0  16.0
[14209]  16.0   0.8  60.0  16.0  16.0   0.9   0.9  16.0  16.0  17.0  68.0  16.0
[14221]  16.0  16.0  16.0   1.0  16.0  16.0  16.0  16.0  82.0  60.0  30.0  16.0
[14233]  16.0  31.0  16.0  21.0  16.0  20.0  12.0  16.0   2.0  16.0   0.9  16.0
[14245]   9.0   4.0   0.3  16.0 119.0  16.0  16.0  60.0  16.0  16.0  40.0  16.0
[14257]   1.0  16.0  31.0  60.0  16.0  31.0   0.9   0.7  61.0  17.0  11.0  16.0
[14269]  16.0  16.0   0.9  16.0  16.0   1.0   6.0  20.0  16.0  15.0  54.0  53.0
[14281]  16.0  31.0   2.0  60.0   1.0  16.0   2.0  16.0   0.9  16.0   0.8  44.0
[14293]  16.0  16.0  16.0  17.0  83.0   2.0  14.0  87.0  49.0   0.9  16.0  61.0
[14305]   0.2  16.0  16.0  16.0  18.0  16.0  47.0   0.9  16.0   1.0  18.0  16.0
[14317]  16.0  16.0  60.0  16.0  16.0  87.0  16.0  31.0  15.0  17.0  16.0  16.0
[14329]  16.0  60.0  16.0  61.0  61.0  16.0  32.0   6.0  16.0  16.0  10.0   2.0
[14341]  16.0  13.0   4.0   0.9   0.9  16.0  16.0   0.9  16.0  61.0  61.0  16.0
[14353]  60.0  83.0  84.0  16.0  16.0  27.0  16.0  32.0  61.0   0.9  84.0   0.0
[14365]  16.0  16.0  16.0  16.0  16.0  16.0  60.0   1.0  16.0  61.0   3.0  16.0
[14377]  16.0  16.0   0.9  91.0  60.0  16.0   2.0  16.0  13.0  16.0   0.9  16.0
[14389]  16.0  16.0  16.0   0.2  16.0   2.0   4.0  61.0  16.0  11.0  16.0  16.0
[14401]  16.0   2.0  16.0  16.0  16.0  16.0  58.0  14.0  16.0   2.0  16.0  16.0
[14413]  16.0  17.0  32.0   4.0  16.0   2.0  16.0  16.0   4.0  17.0  43.0  16.0
[14425]  61.0   0.9  16.0  16.0  16.0  16.0  91.0  16.0  16.0  16.0   5.0  16.0
[14437]  18.0  16.0  16.0  25.0  54.0  16.0  16.0  16.0   4.0  61.0  16.0  32.0
[14449]  32.0  16.0  87.0  61.0   2.0   0.9   4.0   2.0  16.0  16.0  16.0  16.0
[14461]  16.0  61.0  16.0  21.0  32.0  16.0   0.9  16.0  16.0  16.0   0.6  16.0
[14473]   0.8  16.0  16.0  16.0  16.0  15.0  16.0  16.0  17.0  61.0  61.0  16.0
[14485]   1.0  16.0  16.0  17.0  16.0  16.0  17.0  61.0  61.0   2.0  61.0  61.0
[14497]  16.0   0.9  33.0  33.0  17.0  16.0  16.0  17.0  17.0  61.0  16.0   0.9
[14509]   0.9  51.0   0.9  62.0   0.9   4.0   1.0   0.9  20.0  16.0   7.0   4.0
[14521]   0.5   7.0   0.9  20.0  16.0   4.0  16.0  47.0  57.0   0.9  10.0  57.0
[14533]  17.0  16.0   4.0  17.0  18.0  18.0   0.9  16.0   2.0   6.0  62.0  18.0
[14545]  17.0  33.0  85.0  17.0  16.0  33.0   1.0   6.0  17.0  51.0  17.0  17.0
[14557]  88.0   0.9  17.0   0.9  16.0  21.0  17.0   3.0  61.0  17.0  17.0  17.0
[14569]  17.0  17.0  17.0  56.0  55.0  58.0  82.0  17.0  17.0   0.2  36.0  17.0
[14581]  51.0   1.0  18.0  85.0  61.0  26.0   0.3  17.0   0.9  86.0  17.0  17.0
[14593]   0.9   2.0  54.0  62.0  62.0   5.0  18.0  32.0  17.0   9.0  17.0  17.0
[14605]   1.0  17.0  17.0   0.9  17.0  17.0  17.0  17.0  17.0  16.0  17.0  62.0
[14617]  62.0  17.0  17.0   2.0   0.9  17.0  17.0  17.0   0.9   1.0  17.0  62.0
[14629]  35.0  37.0  17.0  26.0  17.0  17.0   0.9  17.0  17.0  44.0  34.0   0.9
[14641]  62.0  17.0  62.0  17.0  17.0  17.0  17.0  17.0  17.0  34.0  17.0  17.0
[14653]  62.0  17.0   2.0   2.0  17.0   2.0   0.9  17.0  22.0  31.0   0.9   0.9
[14665]  59.0  62.0  17.0   0.9  81.0  35.0  17.0   0.9   7.0  56.0  12.0  17.0
[14677]   0.9  13.0   0.9  28.0   0.9  10.0  17.0  17.0  17.0   2.0  17.0  17.0
[14689]   3.0  35.0  62.0   0.9   1.0  17.0  15.0  17.0  17.0  17.0  17.0  17.0
[14701]  17.0   3.0  17.0  62.0  17.0   0.9   3.0  17.0  17.0  17.0  17.0  17.0
[14713]  31.0  17.0  17.0  17.0  17.0  17.0   0.6  17.0  18.0  14.0  62.0  62.0
[14725]  17.0  17.0  33.0  84.0  62.0  17.0  15.0  17.0  17.0  17.0  17.0  62.0
[14737]  58.0  60.0  17.0   0.9   9.0   2.0  62.0   0.9  17.0   0.9  17.0   5.0
[14749]  62.0  23.0  62.0  62.0  62.0  17.0   0.9  16.0  17.0  17.0  63.0  17.0
[14761]  63.0  62.0   0.9  17.0   0.9  17.0  17.0  17.0  17.0  18.0  19.0  24.0
[14773]  17.0  17.0   1.0  17.0  85.0  17.0  17.0  33.0  17.0  17.0   6.0  87.0
[14785]  17.0  17.0  17.0  63.0  17.0   0.2  18.0  17.0   0.9  17.0  18.0  17.0
[14797]   6.0   2.0   2.0  19.0  17.0  17.0   0.8  17.0  35.0  17.0  63.0  17.0
[14809]  63.0   2.0  17.0   2.0  17.0  17.0  63.0  17.0  17.0   0.9   0.9   0.9
[14821]  33.0   1.0   1.0   0.1  17.0  17.0  14.0  17.0  17.0   0.9   0.2  17.0
[14833]  17.0  17.0   3.0  37.0  34.0  62.0   2.0  35.0  88.0   0.5  34.0  34.0
[14845]   7.0   0.9  63.0  17.0  17.0   0.9   2.0  17.0  18.0   2.0  34.0  17.0
[14857]   0.9   0.9  17.0  35.0  63.0  57.0  17.0  22.0  60.0   0.9  36.0   1.0
[14869]   0.9  17.0  17.0  17.0  17.0  17.0   4.0  31.0   0.9  17.0  17.0  17.0
[14881]  17.0  17.0  17.0  19.0  17.0  23.0   9.0  17.0  63.0   4.0   0.9   1.0
[14893]   1.0  17.0  86.0   0.9  25.0  12.0  17.0  17.0   1.0   0.2   5.0   0.9
[14905]   8.0  17.0  18.0  17.0   0.0  58.0  17.0   0.9  17.0   0.9  18.0   0.9
[14917]  63.0  17.0  17.0  11.0  17.0   0.9   0.7  24.0  24.0  17.0  63.0  35.0
[14929]  17.0  36.0  17.0  17.0  17.0   0.5  17.0  14.0   0.9  17.0  17.0   1.0
[14941]   0.3  17.0  17.0  17.0  17.0  17.0  58.0  63.0  63.0   6.0  17.0   2.0
[14953]  17.0 119.0  17.0  17.0  17.0  17.0   2.0  17.0  17.0  17.0  14.0  17.0
[14965]  17.0  17.0  31.0  63.0  17.0  29.0  64.0   6.0   3.0  17.0   0.1   0.9
[14977]  17.0  17.0  17.0  17.0  17.0  17.0  17.0  17.0   0.9  17.0  17.0  64.0
[14989]  17.0   1.0  18.0  17.0   0.9   0.9  17.0   0.2  86.0  17.0   0.7  23.0
[15001]   1.0  17.0  64.0  17.0  64.0   0.9   0.0  17.0  22.0  17.0 138.0  16.0
[15013]  17.0  35.0  60.0   1.0  64.0  64.0  17.0  64.0  17.0  36.0   0.9   0.9
[15025]  32.0  14.0  17.0  17.0  17.0   6.0  16.0   0.9   1.0   1.0   3.0  17.0
[15037]   0.9  14.0  64.0   0.9   6.0  24.0  17.0  17.0  19.0  17.0  17.0   0.4
[15049]  17.0  17.0  17.0   0.9  17.0  17.0  65.0  17.0  17.0   4.0   7.0  15.0
[15061]  24.0   1.0  10.0   9.0  17.0   0.9  17.0  89.0   9.0  10.0  17.0  17.0
[15073]  22.0  17.0  17.0  35.0  65.0  65.0  65.0  17.0  17.0   0.9  17.0  87.0
[15085]  17.0  17.0  66.0  64.0  58.0  64.0  65.0  66.0  66.0  17.0  66.0  17.0
[15097]  17.0   7.0   3.0  17.0  17.0  66.0  17.0  17.0  66.0  17.0  17.0  17.0
[15109]   0.9   2.0  17.0  17.0  17.0   0.9  60.0  17.0   0.8  17.0  17.0  90.0
[15121]  35.0  66.0   0.9   5.0  17.0  17.0   4.0  17.0  89.0  17.0  17.0  17.0
[15133]  17.0  17.0  11.0  66.0   0.9   0.9   2.0  17.0   2.0   0.9   0.9  17.0
[15145]  17.0   0.2  17.0  17.0  17.0  36.0  38.0  17.0  17.0   2.0   0.9  17.0
[15157]   0.9  36.0  17.0  17.0  17.0  36.0  17.0  17.0  17.0  17.0  17.0  67.0
[15169]   0.2   3.0  18.0  17.0  17.0   0.0  16.0  17.0   1.0  38.0  17.0  58.0
[15181]   1.0  31.0  38.0   0.5  17.0  17.0  17.0  17.0  34.0  15.0  17.0  63.0
[15193]  58.0  59.0  17.0   6.0  17.0  17.0   2.0   0.9  17.0  17.0  36.0  17.0
[15205]  17.0  87.0  66.0  17.0  17.0  18.0  17.0  87.0   0.9  17.0  17.0  17.0
[15217]  17.0  17.0  17.0  17.0  66.0  67.0  19.0  18.0  17.0   0.3  36.0  21.0
[15229]  17.0  17.0  87.0  17.0  17.0  39.0   4.0   1.0  17.0  67.0   3.0  17.0
[15241]  36.0  90.0  17.0  17.0   0.9  88.0  88.0   0.9   9.0  17.0   0.0   4.0
[15253]  15.0  17.0  67.0  17.0  67.0  17.0  17.0  37.0  17.0  39.0   1.0  37.0
[15265]  17.0  67.0  37.0  17.0   0.3   0.9  17.0  17.0  17.0  67.0  17.0  68.0
[15277]   0.7   0.5  89.0  17.0   3.0  17.0  18.0  17.0   0.9  17.0   4.0  17.0
[15289]  18.0  17.0  17.0  17.0  17.0   4.0  17.0   0.9  17.0  14.0  17.0   0.9
[15301]  64.0  17.0  67.0  68.0   0.2  89.0  17.0  17.0   5.0  17.0  17.0   3.0
[15313]  17.0   0.9  17.0  17.0  17.0  17.0  60.0  17.0  36.0   4.0   0.0  17.0
[15325]  37.0  17.0   0.9  37.0   9.0  17.0  40.0  24.0  90.0  90.0  17.0  88.0
[15337]  39.0  17.0  18.0  17.0   0.9  17.0   0.8   0.9   0.9  40.0   0.6  17.0
[15349]  17.0   6.0   1.0   0.4  39.0  17.0  17.0   0.8  17.0  17.0  18.0  17.0
[15361]  17.0  17.0  69.0  62.0  69.0  17.0   4.0   6.0   3.0  40.0  17.0  17.0
[15373]  17.0   0.8   0.7  17.0   7.0  17.0  71.0  42.0   9.0  36.0  14.0  90.0
[15385]  11.0  39.0  17.0   0.9  17.0   0.9  17.0  17.0   0.3  23.0   0.9  39.0
[15397]  15.0  17.0   4.0  17.0  19.0  17.0  40.0  17.0  17.0  41.0  17.0  66.0
[15409]  17.0  17.0  45.0  34.0  72.0   0.9   0.9   0.9  13.0   0.9  17.0  17.0
[15421]   0.4  31.0  17.0  17.0   0.8   0.9   0.9  17.0  16.0  17.0   1.0   2.0
[15433]   0.9  40.0  17.0  41.0  65.0   0.9  59.0  61.0  68.0  68.0   0.8  17.0
[15445]  70.0  17.0  17.0   4.0  18.0  17.0  17.0  67.0  17.0  17.0  25.0   1.0
[15457]  17.0  17.0  17.0   0.9   0.9   0.9   3.0  17.0  17.0   0.9   0.9   2.0
[15469]  17.0   4.0  41.0  17.0  17.0  17.0  17.0  11.0   0.9   3.0   0.9  17.0
[15481]  30.0  17.0  29.0  13.0  17.0  48.0  17.0  17.0  17.0  17.0  17.0  71.0
[15493]  75.0  49.0  74.0  10.0  43.0   0.6   0.1  17.0  17.0  53.0  71.0  17.0
[15505]  17.0  44.0  17.0   0.6   0.9   0.4  77.0  17.0   5.0  45.0  18.0  19.0
[15517]   0.9   0.6  17.0  17.0  18.0  69.0  69.0  72.0

  • <- representa una flecha que se lee: asignar a.

  • Permite guardar un objeto con un nombre dado

    • El objeto se guarda en el Environment.
genero <- covid_testing$gender
  • Luego, lo puedes llamar utilizando el nombre con el que lo guardaste.
genero
    [1] "female" "female" "male"   "female" "male"   "female" "male"   "female"
    [9] "male"   "male"   "male"   "female" "female" "male"   "female" "female"
   [17] "male"   "male"   "female" "female" "female" "male"   "female" "male"  
   [25] "female" "male"   "female" "male"   "female" "female" "male"   "female"
   [33] "female" "female" "female" "female" "male"   "female" "male"   "female"
   [41] "female" "male"   "male"   "female" "female" "male"   "male"   "male"  
   [49] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
   [57] "female" "female" "female" "female" "male"   "male"   "male"   "female"
   [65] "female" "female" "male"   "female" "male"   "female" "male"   "male"  
   [73] "female" "female" "female" "male"   "male"   "male"   "female" "male"  
   [81] "female" "female" "female" "female" "female" "male"   "female" "male"  
   [89] "male"   "male"   "male"   "female" "male"   "male"   "female" "female"
   [97] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
  [105] "female" "female" "male"   "male"   "female" "male"   "male"   "male"  
  [113] "female" "female" "male"   "male"   "male"   "female" "female" "male"  
  [121] "female" "female" "male"   "male"   "female" "female" "female" "male"  
  [129] "female" "female" "male"   "female" "female" "female" "female" "male"  
  [137] "male"   "male"   "male"   "male"   "male"   "female" "female" "male"  
  [145] "male"   "female" "male"   "female" "female" "male"   "male"   "male"  
  [153] "female" "female" "female" "male"   "female" "male"   "male"   "female"
  [161] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
  [169] "female" "male"   "female" "female" "female" "male"   "female" "male"  
  [177] "female" "female" "male"   "male"   "female" "female" "female" "female"
  [185] "female" "male"   "male"   "male"   "female" "male"   "male"   "female"
  [193] "female" "male"   "male"   "male"   "male"   "male"   "male"   "female"
  [201] "female" "female" "female" "female" "male"   "male"   "male"   "male"  
  [209] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
  [217] "male"   "female" "male"   "male"   "male"   "female" "male"   "male"  
  [225] "male"   "male"   "female" "female" "female" "female" "female" "male"  
  [233] "female" "male"   "male"   "female" "female" "male"   "female" "female"
  [241] "female" "male"   "female" "female" "male"   "male"   "female" "male"  
  [249] "female" "female" "female" "male"   "female" "female" "female" "male"  
  [257] "male"   "female" "male"   "female" "male"   "female" "male"   "male"  
  [265] "female" "female" "male"   "male"   "female" "female" "male"   "male"  
  [273] "female" "male"   "male"   "male"   "male"   "male"   "male"   "male"  
  [281] "female" "female" "male"   "female" "female" "female" "female" "female"
  [289] "female" "female" "male"   "female" "female" "male"   "male"   "female"
  [297] "female" "female" "female" "female" "male"   "female" "male"   "female"
  [305] "female" "male"   "male"   "male"   "male"   "female" "male"   "female"
  [313] "male"   "female" "male"   "male"   "female" "female" "male"   "female"
  [321] "female" "female" "female" "female" "male"   "male"   "male"   "female"
  [329] "female" "male"   "male"   "female" "female" "female" "male"   "male"  
  [337] "female" "male"   "female" "female" "male"   "male"   "male"   "male"  
  [345] "female" "male"   "male"   "male"   "female" "male"   "female" "female"
  [353] "female" "female" "female" "female" "female" "male"   "female" "male"  
  [361] "male"   "male"   "male"   "female" "male"   "male"   "female" "male"  
  [369] "female" "female" "female" "male"   "female" "female" "male"   "male"  
  [377] "female" "female" "female" "male"   "male"   "male"   "male"   "female"
  [385] "male"   "female" "male"   "female" "female" "male"   "female" "male"  
  [393] "female" "female" "male"   "female" "female" "male"   "female" "female"
  [401] "female" "female" "male"   "male"   "male"   "female" "female" "male"  
  [409] "male"   "female" "male"   "female" "male"   "male"   "male"   "female"
  [417] "female" "male"   "male"   "male"   "female" "male"   "female" "male"  
  [425] "female" "male"   "female" "female" "male"   "female" "female" "female"
  [433] "male"   "male"   "male"   "male"   "male"   "male"   "male"   "male"  
  [441] "male"   "male"   "male"   "female" "female" "male"   "male"   "male"  
  [449] "male"   "male"   "male"   "female" "male"   "female" "female" "male"  
  [457] "female" "female" "male"   "female" "male"   "male"   "male"   "female"
  [465] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
  [473] "female" "male"   "male"   "female" "female" "female" "female" "female"
  [481] "female" "female" "female" "female" "male"   "female" "female" "female"
  [489] "female" "female" "female" "female" "female" "male"   "male"   "female"
  [497] "female" "male"   "male"   "male"   "female" "female" "male"   "male"  
  [505] "female" "male"   "male"   "male"   "male"   "female" "male"   "female"
  [513] "female" "female" "female" "female" "female" "male"   "female" "male"  
  [521] "female" "female" "female" "female" "female" "male"   "female" "female"
  [529] "female" "female" "female" "female" "female" "male"   "male"   "male"  
  [537] "female" "male"   "female" "male"   "female" "female" "male"   "female"
  [545] "male"   "male"   "female" "female" "female" "male"   "male"   "female"
  [553] "male"   "male"   "female" "female" "male"   "male"   "male"   "male"  
  [561] "male"   "female" "female" "female" "male"   "female" "female" "female"
  [569] "male"   "female" "female" "male"   "male"   "female" "female" "female"
  [577] "female" "female" "female" "female" "male"   "male"   "female" "male"  
  [585] "female" "female" "female" "male"   "female" "female" "female" "female"
  [593] "female" "female" "male"   "female" "female" "female" "male"   "male"  
  [601] "female" "female" "male"   "female" "male"   "female" "male"   "female"
  [609] "female" "female" "female" "female" "female" "male"   "male"   "male"  
  [617] "female" "female" "female" "female" "male"   "male"   "female" "female"
  [625] "male"   "male"   "male"   "female" "female" "female" "female" "male"  
  [633] "female" "female" "female" "male"   "male"   "male"   "male"   "female"
  [641] "male"   "male"   "male"   "female" "male"   "male"   "male"   "female"
  [649] "male"   "male"   "male"   "male"   "female" "male"   "male"   "female"
  [657] "male"   "male"   "male"   "female" "male"   "male"   "female" "male"  
  [665] "female" "male"   "male"   "female" "male"   "female" "female" "male"  
  [673] "female" "female" "male"   "female" "male"   "female" "female" "male"  
  [681] "male"   "female" "male"   "male"   "female" "female" "female" "female"
  [689] "male"   "male"   "male"   "female" "male"   "female" "male"   "female"
  [697] "male"   "female" "female" "female" "female" "female" "female" "male"  
  [705] "male"   "male"   "female" "female" "female" "female" "female" "male"  
  [713] "female" "male"   "male"   "female" "female" "female" "male"   "female"
  [721] "male"   "male"   "female" "female" "female" "male"   "female" "female"
  [729] "male"   "female" "female" "female" "female" "male"   "female" "female"
  [737] "male"   "male"   "female" "female" "male"   "male"   "male"   "male"  
  [745] "female" "female" "male"   "female" "male"   "female" "female" "female"
  [753] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
  [761] "female" "male"   "male"   "male"   "male"   "male"   "male"   "female"
  [769] "male"   "female" "male"   "male"   "female" "male"   "male"   "male"  
  [777] "female" "female" "female" "female" "female" "male"   "female" "female"
  [785] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
  [793] "female" "female" "female" "male"   "male"   "female" "female" "female"
  [801] "female" "male"   "female" "male"   "female" "female" "female" "male"  
  [809] "female" "female" "female" "female" "female" "male"   "male"   "male"  
  [817] "female" "female" "male"   "male"   "male"   "female" "female" "female"
  [825] "male"   "female" "male"   "female" "male"   "female" "male"   "male"  
  [833] "female" "female" "female" "male"   "female" "female" "female" "male"  
  [841] "female" "male"   "female" "female" "male"   "female" "female" "female"
  [849] "male"   "male"   "male"   "female" "male"   "female" "male"   "male"  
  [857] "female" "male"   "male"   "female" "female" "male"   "female" "female"
  [865] "male"   "female" "male"   "male"   "male"   "female" "female" "female"
  [873] "male"   "male"   "female" "female" "female" "male"   "male"   "female"
  [881] "male"   "female" "female" "female" "female" "male"   "female" "female"
  [889] "male"   "male"   "female" "female" "female" "female" "male"   "female"
  [897] "male"   "female" "male"   "female" "female" "female" "female" "female"
  [905] "female" "female" "male"   "female" "female" "male"   "female" "female"
  [913] "female" "male"   "female" "female" "male"   "male"   "female" "male"  
  [921] "female" "female" "male"   "male"   "female" "female" "male"   "female"
  [929] "female" "male"   "male"   "male"   "female" "male"   "male"   "male"  
  [937] "male"   "male"   "female" "male"   "female" "male"   "male"   "male"  
  [945] "male"   "male"   "male"   "male"   "male"   "male"   "male"   "female"
  [953] "male"   "male"   "male"   "male"   "male"   "male"   "male"   "female"
  [961] "male"   "female" "male"   "male"   "female" "female" "female" "male"  
  [969] "female" "female" "female" "male"   "male"   "male"   "male"   "female"
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[14625] "female" "female" "male"   "female" "male"   "female" "male"   "female"
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[14737] "female" "female" "female" "male"   "male"   "female" "female" "female"
[14745] "male"   "male"   "female" "male"   "male"   "female" "male"   "male"  
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[14761] "female" "female" "female" "female" "male"   "male"   "male"   "male"  
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[14777] "female" "male"   "female" "male"   "male"   "female" "female" "male"  
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[14793] "female" "female" "female" "female" "female" "female" "male"   "female"
[14801] "male"   "male"   "female" "female" "female" "female" "female" "female"
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[14817] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
[14825] "female" "male"   "male"   "male"   "male"   "male"   "male"   "male"  
[14833] "male"   "female" "female" "female" "male"   "male"   "male"   "female"
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[14849] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[14857] "male"   "male"   "female" "male"   "male"   "female" "female" "male"  
[14865] "male"   "male"   "female" "female" "male"   "male"   "female" "female"
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[14889] "male"   "male"   "female" "female" "male"   "female" "female" "female"
[14897] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
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[14913] "female" "male"   "female" "female" "male"   "female" "male"   "male"  
[14921] "male"   "female" "male"   "female" "male"   "female" "male"   "female"
[14929] "female" "female" "male"   "male"   "female" "male"   "male"   "female"
[14937] "male"   "female" "female" "female" "male"   "male"   "male"   "male"  
[14945] "female" "male"   "male"   "female" "male"   "male"   "female" "female"
[14953] "male"   "female" "male"   "female" "male"   "male"   "female" "male"  
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[14985] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
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[15009] "female" "male"   "female" "male"   "female" "male"   "female" "female"
[15017] "male"   "male"   "female" "male"   "male"   "female" "male"   "male"  
[15025] "female" "female" "male"   "female" "male"   "male"   "female" "female"
[15033] "female" "male"   "female" "female" "female" "female" "male"   "male"  
[15041] "male"   "female" "female" "female" "female" "male"   "female" "male"  
[15049] "male"   "male"   "male"   "male"   "female" "male"   "male"   "male"  
[15057] "male"   "female" "female" "female" "female" "female" "male"   "female"
[15065] "male"   "female" "male"   "male"   "male"   "female" "male"   "male"  
[15073] "male"   "male"   "female" "male"   "male"   "male"   "male"   "male"  
[15081] "female" "male"   "male"   "female" "male"   "female" "male"   "female"
[15089] "female" "female" "female" "female" "female" "female" "male"   "female"
[15097] "female" "female" "male"   "male"   "male"   "male"   "female" "male"  
[15105] "female" "female" "male"   "male"   "male"   "female" "male"   "female"
[15113] "female" "female" "female" "female" "male"   "female" "male"   "male"  
[15121] "male"   "male"   "female" "male"   "female" "female" "male"   "male"  
[15129] "male"   "male"   "female" "female" "female" "female" "male"   "female"
[15137] "male"   "female" "male"   "female" "male"   "male"   "male"   "male"  
[15145] "female" "female" "male"   "female" "female" "female" "female" "male"  
[15153] "male"   "female" "female" "male"   "female" "male"   "male"   "male"  
[15161] "male"   "male"   "female" "male"   "male"   "female" "female" "female"
[15169] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[15177] "male"   "female" "male"   "female" "male"   "male"   "female" "female"
[15185] "female" "male"   "male"   "female" "female" "female" "male"   "female"
[15193] "male"   "male"   "female" "female" "female" "male"   "male"   "male"  
[15201] "male"   "female" "male"   "female" "female" "female" "male"   "female"
[15209] "male"   "male"   "male"   "male"   "female" "male"   "male"   "female"
[15217] "male"   "female" "female" "female" "male"   "female" "female" "female"
[15225] "male"   "female" "male"   "male"   "male"   "female" "female" "male"  
[15233] "male"   "female" "male"   "male"   "male"   "female" "male"   "male"  
[15241] "male"   "male"   "female" "female" "male"   "female" "female" "female"
[15249] "male"   "female" "female" "male"   "male"   "male"   "female" "female"
[15257] "male"   "female" "female" "male"   "female" "female" "female" "male"  
[15265] "male"   "male"   "male"   "female" "male"   "male"   "male"   "female"
[15273] "male"   "female" "male"   "female" "female" "male"   "female" "female"
[15281] "female" "female" "female" "male"   "female" "female" "male"   "male"  
[15289] "male"   "female" "female" "male"   "female" "female" "male"   "male"  
[15297] "female" "female" "male"   "male"   "male"   "female" "male"   "male"  
[15305] "male"   "female" "male"   "male"   "female" "female" "male"   "female"
[15313] "male"   "male"   "female" "male"   "female" "female" "female" "female"
[15321] "male"   "female" "male"   "male"   "female" "female" "female" "male"  
[15329] "female" "male"   "female" "female" "female" "female" "female" "female"
[15337] "male"   "male"   "female" "female" "female" "male"   "male"   "male"  
[15345] "male"   "female" "female" "male"   "male"   "female" "female" "female"
[15353] "male"   "male"   "male"   "male"   "female" "male"   "female" "male"  
[15361] "female" "female" "male"   "female" "female" "male"   "male"   "male"  
[15369] "female" "female" "female" "female" "male"   "male"   "male"   "female"
[15377] "male"   "male"   "male"   "female" "male"   "female" "male"   "male"  
[15385] "male"   "male"   "female" "female" "female" "female" "male"   "male"  
[15393] "male"   "male"   "male"   "male"   "male"   "female" "male"   "female"
[15401] "female" "male"   "female" "male"   "male"   "female" "male"   "female"
[15409] "female" "male"   "male"   "female" "male"   "male"   "male"   "female"
[15417] "male"   "female" "female" "male"   "male"   "female" "female" "female"
[15425] "female" "female" "female" "male"   "female" "male"   "male"   "female"
[15433] "male"   "male"   "female" "female" "male"   "male"   "male"   "male"  
[15441] "female" "female" "male"   "female" "female" "male"   "female" "female"
[15449] "male"   "female" "male"   "female" "female" "male"   "male"   "male"  
[15457] "male"   "male"   "male"   "female" "male"   "female" "male"   "male"  
[15465] "male"   "male"   "male"   "female" "female" "female" "female" "female"
[15473] "female" "female" "female" "male"   "female" "male"   "male"   "male"  
[15481] "female" "female" "male"   "female" "male"   "female" "male"   "female"
[15489] "female" "female" "male"   "female" "female" "female" "male"   "male"  
[15497] "male"   "female" "male"   "male"   "female" "female" "male"   "female"
[15505] "male"   "male"   "female" "female" "male"   "male"   "female" "female"
[15513] "male"   "female" "male"   "male"   "male"   "female" "female" "female"
[15521] "female" "female" "male"   "female"

El panel Environment

Es un panel que muestra los objetos (data frames, matrices, vectores, funciones, etc.) que se tienen en el entorno de trabajo.

Imprimir y guardar resultado

Nuestro turno

  • Use la función de ayuda para obtener información de algunos paquetes.

  • Instale algunos paquetes. Por ejemplo, dslabs.

  • Cargue algunos paquetes.

  • Cargue los datos gapminder del paquete dslabs.

  • Selecciona algunas columnas y asignelas a nuevos objetos.

  • Use la función round() para redondear números.

    • Consulte el menú de ayuda para ver cómo funcionan.

  • Descargue el metapaquete tidyverse.


30:00

¡Gracias!
¿Preguntas?




@psotob91

https://github.com/psotob91

percys1991@gmail.com

Curso: R para usuarios de Stata - Ed. 1 (2023)